https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/issue/feed 2019-06-05T12:57:33+00:00 Open Journal Systems <p>Faculty of Technology, Research Groups in Informatics</p> https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/258 2019-06-05T12:57:33+00:00 Nathalie Villa ojs.ub@uni-bielefeld.de Fabrice Rossi ojs.ub@uni-bielefeld.de

Flexible and efficient variants of the Self Organizing Map algorithm have been proposed for non vector data, including, for example, the dissimilarity SOM (also called the Median SOM) and several kernelized versions of SOM. Although the first one is a generalization of the batch version of the SOM algorithm to data described by a dissimilarity measure, the various versions of the second ones are stochastic SOM. We propose here to introduce a batch version of the kernel SOM and to show how this one is related to the dissimilarity SOM. Finally, an application to the classification of the vertices of a graph is proposed and the algorithms are tested and compared on a simulated data set.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/259 2019-06-05T12:57:33+00:00 Danilo Di Stefano ojs.ub@uni-bielefeld.de Valentino Pediroda ojs.ub@uni-bielefeld.de

In this paper a new method for financial time series discretization that allows to take into account qualitative features about financial indicators is proposed. Qualitative features are extracted from financial news web sites and they are inserted into the learning phase of a recursive Self Organizing Map by means of a suitable parameter derived from statistical analysis of document collections. A postprocessing phase based on unsupervised clustering by U-Matrix method leads to the actual discretization of the time series. A real case application to a stock closing price series reveals that the inclusion of qualitative features leads to a more compact discretization of the series. This could be useful if a compact coding of the series is sought, for example in the preprocessing phase of a forecasting methodology.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/260 2019-06-05T12:57:32+00:00 Fabrice Rossi ojs.ub@uni-bielefeld.de Alexander Hasenfuß ojs.ub@uni-bielefeld.de Barbara Hammer ojs.ub@uni-bielefeld.de

In some application contexts, data are better described by a matrix of pairwise dissimilarities rather than by a vector representation. Clustering and topographic mapping algorithms have been adapted to this type of data, either via the generalized Median principle, or more recently with the so called relational approach, in which prototypes are represented by virtual linear combinations of the original observations. One drawback of those methods is their complexity, which scales as the square of the number of observations, mainly because they use dense prototype representations: each prototype is obtained as a virtual combination of all the elements of its cluster (at least). We propose in this paper to use a sparse representation of the prototypes to obtain relational algorithms with sub-quadratic complexity.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/261 2019-06-05T12:57:31+00:00 Petra Schneider ojs.ub@uni-bielefeld.de Michael Biehl ojs.ub@uni-bielefeld.de Frank-Michael Schleif ojs.ub@uni-bielefeld.de Barbara Hammer ojs.ub@uni-bielefeld.de

Metric adaptation constitutes a powerful approach to improve the performance of prototype based classication schemes. We apply extensions of Generalized LVQ based on different adaptive distance measures in the domain of clinical proteomics. The Euclidean distance in GLVQ is extended by adaptive relevance vectors and matrices of global or local influence where training follows a stochastic gradient descent on an appropriate error function. We compare the performance of the resulting learning algorithms for the classification of high dimensional mass spectrometry data from cancer research. High prediction accuracies can be obtained by adapting full matrices of relevance factors in the distance measure in order to adjust the metric to the underlying data structure. The easy interpretability of the resulting models after training of relevance vectors allows to identify discriminative features in the original spectra.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/262 2019-06-05T12:57:03+00:00 Hujun Yin ojs.ub@uni-bielefeld.de

The self-organizing map (SOM) and some of its variants such as visualization induced SOM (ViSOM) have been shown to yield similar results to multidimensional scaling (MDS). However the exact connection has yet been established. In this paper we first examine their relationship with (generalized) MDS from their cost functions in the aspect of data visualization and dimensionality reduction. The SOM is shown to produce a quantized, qualitative or nonmetric scaling and while the ViSOM is a quantitative metric scaling. Then we propose a way to use the core principle of the ViSOM, i.e. local distance preserving, to adaptively and incrementally construct a metric local scaling and to extract nonlinear manifold. Comparison with other methods such as ISOMAP and LLE has been made, especially in mapping highly nonlinear subspaces. The advantages over other methods are also discussed.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/263 2019-06-05T12:57:01+00:00 Hideaki Misawa ojs.ub@uni-bielefeld.de Takeshi Yamakawa ojs.ub@uni-bielefeld.de

In this paper we propose an energy function-based optimization method in order to improve the approximation ability of the self-organizing relationship (SOR) network. In the execution mode, the SOR network can be used as a fuzzy inference engine. The output of the SOR network is calculated by using the reference vectors and matching parameters. The matching parameters, which correspond to the standard deviation of the Gaussian membership function used in fuzzy inference, are only defined in the execution mode. However, the issue of the optimization of the matching parameters has not yet been treated in previous works. To optimize the matching parameters, we introduce an energy function to the SOR network. The energy function can be used to tune not only the matching parameters but also the reference vectors with a gradient descent method. The proposed method is applied to a function approximation problem and the improvement of the approximation ability is confirmed.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/264 2019-06-05T12:57:00+00:00 Heizo Tokutaka ojs.ub@uni-bielefeld.de Yoshio Maniwa ojs.ub@uni-bielefeld.de P. K. Kihato ojs.ub@uni-bielefeld.de Kikuo Fujimura ojs.ub@uni-bielefeld.de Masaaki Ohkita ojs.ub@uni-bielefeld.de

A health evaluation system was constructed which visualizes the living habits and health state from a person's checkup list by using the feature of SOM that multi-dimensional data can be mapped onto a two-dimensional surface. Here, three examples cases are reported. A change to the health region of the map by taking medication was visualized by the SOM from the conventional numerical expression. Also, the specific sick record converges towards the sick region of the map when the disease progresses. However, it was shown and visualized for the sick record not to converge in the case of the metastasis of a cancer even if for the same examinee, the cancer has progressed. Finally, for the display of the health point mark, and the display of the sick record, the spherical surface SOM, is demonstrated to be suited in the visualization.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/265 2019-06-05T12:56:59+00:00 Takashi Abe ojs.ub@uni-bielefeld.de Shun Ikeda ojs.ub@uni-bielefeld.de Shigehiko Kanaya ojs.ub@uni-bielefeld.de Kennosuke Wada ojs.ub@uni-bielefeld.de Toshimichi Ikemura ojs.ub@uni-bielefeld.de

An unsupervised clustering algorithm Kohonen's SOM is an effective tool for clustering and visualizing high-dimensional complex data on a single map. We previously modified the conventional SOM for genome informatics, making the learning process and resulting map independent of the order of data input on the basis of Batch Learning SOM (BL-SOM). We generated BL-SOMs for tetra- and pentanucleotide frequencies in 300,000 10-kb sequences from 13 eukaryotes for which almost complete genomic sequences are available. BL-SOM recognized species-specific characteristics of oligonucleotide frequencies in most 10-kb sequences, permitting species-specific classification of sequences without any information regarding the species. We next constructed BL-SOMs with tetra- and pentanucleotide frequencies in 37,086 full-length mouse cDNA sequences. With BL-SOM we also analyzed occurrence patterns of the oligonucleotides that are thought to be involved in transcriptional regulation on the human genome.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/266 2019-06-05T12:56:58+00:00 Thomas Villmann ojs.ub@uni-bielefeld.de Frank-Michael Schleif ojs.ub@uni-bielefeld.de E. Merenyi ojs.ub@uni-bielefeld.de M. Strickert ojs.ub@uni-bielefeld.de Barbara Hammer ojs.ub@uni-bielefeld.de

We propose an extension of the self-organizing map for supervised fuzzy classification learning, whereby uncertain (fuzzy) class information is also allowed for training data. The method is able to detect class similarities, which can be used for data vizualization. Applying a special functional metric, derived from of the L_p norms, we show the application of the method for classification and visualization of hyper-spectral data in satellite image remote sensing image analysis.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/267 2019-06-05T12:56:57+00:00 Miguel Arturo Barreto Sanz ojs.ub@uni-bielefeld.de Andres Pérez-Uribe ojs.ub@uni-bielefeld.de

A technique called component planes is commonly used to visualize variables behavior with Self Organizing Map (SOM). A methodology to clustering the component planes based on the SOM distance matrix is presented. This methodology is used in order to classify zones with similar agro-ecological conditions in the sugar cane culture. Analyzing the obtained groups it was possible to extract new knowledge about the relationship between the agro-ecological variables and productivity.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/268 2019-06-05T12:56:56+00:00 Heizo Tokutaka ojs.ub@uni-bielefeld.de P. K. Kihato ojs.ub@uni-bielefeld.de Kikuo Fujimura ojs.ub@uni-bielefeld.de Masaaki Ohkita ojs.ub@uni-bielefeld.de

A cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wine recognition data sets are used. As a result of the numerical experiment, a clustering method using the dendrogram yielded 97 % in accuracy. It is difficult to display a multi-dimensional data by the dendrogram in one dimension. The ultimate visualization is by means of 3 dimensional rendition. We conclude that the best way that a multi-dimensional data set is visualized is by a sphere, since the phase relationship of it is smooth everywhere.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/269 2019-06-05T12:56:55+00:00 Robert Neumayer ojs.ub@uni-bielefeld.de Rudolf Mayer ojs.ub@uni-bielefeld.de Andreas Rauber ojs.ub@uni-bielefeld.de

Self-Organising Maps have been used for a wide range of clustering applications. They are well-suited for various visualisation techniques to offer better insight into the clustered data sets. A particularly feasible visualisation is the plotting of single components of a data set and their distribution across the SOM. One central problem of the visualisation of Component Planes is that a single plot is needed for each component; this understandably leads to problems with higher-dimensional data. We therefore build on the Metro Visualisation for Self-Organising Maps which integrates the idea of Component Planes into one illustration. Higher-dimensional data sets still pose problems in terms of overloaded visualisations - component selection and aggregation techniques are highly desirable. We therefore propose and compare two methods, one for the aggregation of correlated components, one for the selection of the components most feasible for visualisation for a given clustering.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/270 2019-06-05T12:56:54+00:00 Olga Nechaeva ojs.ub@uni-bielefeld.de

In this paper, an important application of Self-Organizing Maps (SOM) to construction of adaptive meshes is considered. It is shown that application of the basic SOM model leads to a number of problems like inaccurate fitting the border of a physical domain, mesh self-crossings, etc. The composite SOM model is proposed which is based on the composition of a number of SOM models interacting in a special way and self-organizing over their own set of input data. A core of the composite SOM model is the colored SOM model with nonadjustable neurons which provides us a technique to control the neuron weights adjustment taking into account the fixed ones and the general layout of the mesh. As a result, the composite SOM model allows us to approximate an arbitrary complex physical domains with well topology preservation.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/271 2019-06-05T12:56:53+00:00 Georg Pölzlbauer ojs.ub@uni-bielefeld.de Thomas Lidy ojs.ub@uni-bielefeld.de Andreas Rauber ojs.ub@uni-bielefeld.de

We present a classifier algorithm that approximates the decision surface of labeled data by a patchwork of separating hyperplanes. The hyperplanes are arranged in a way inspired by how Self-Organizing Maps are trained. We take advantage of the fact that the boundaries can often be approximated by linear ones connected by a low-dimensional nonlinear manifold. The resulting classifier allows for a voting scheme that averages over the classifiction results of neighboring hyperplanes. Our algorithm is computationally efficient both in terms of training and classification. Further, we present a model selection framework for estimation of the paratmeters of the classification boundary, and show results for artificial and real-world data sets.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/272 2019-06-05T12:56:52+00:00 Teuvo Kohonen ojs.ub@uni-bielefeld.de

This paper introduces a novel way of analyzing input patterns presented to the Self-Organizing Map (SOM). Instead of identifying only the "winner," i.e., the model that matches best with the input, we determine the linear mixture of the models (reference vectors) of the SOM that approximates to the input vector best. It will be shown that if only nonnegative weights are allowed in this linear mixture, the expansion of the input pattern in terms of the models is very meaningful, contains only few terms, and provides a better insight into the input state than what the mere "winner" can give. If then the models fall into classes that are known a priori, the sums of the weights over each class can be interpreted as expressing the affiliation of the input with the due classes.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/273 2019-06-05T12:56:51+00:00 Leticia Maria Seijas ojs.ub@uni-bielefeld.de Enrique Carlos Segura ojs.ub@uni-bielefeld.de

This work presents a system for pattern recognition that combines a self-organising unsupervised technique (via a Kohonen-type SOM) with a bayesian strategy in order to classify input patterns from a given probability distribution and, at the same time, detect ambiguous cases and explain answers. We apply the system to the recognition of handwritten digits. This proposal is intended as an improvement of a model previously introduced by our group, consisting basically of a hybrid unsupervised, self-organising model, followed by a supervised stage. Experiments were carried out on the handwritten digit database of the Concordia University, which is generally accepted as one of the standards in most of the literature in the field.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/274 2019-06-05T12:56:50+00:00 Leonardo Aguayo ojs.ub@uni-bielefeld.de Guilherme A. Barreto ojs.ub@uni-bielefeld.de

This paper introduces the DANTE project: Detection of Anomalies and Novelties in Time sEries with self-organizing networks. The goal of this project is to evaluate several self-organizing networks in the detection of anomalies/novelties in dynamic data patterns. For this purpose, we first describe three standard clustering-based approaches which uses well-known self-organizing neural architectures, such as the SOM and the Fuzzy ART algorithms, and then present a novel approach based on the Operator Map (OPM) network. The OPM is a generalization of the SOM where neurons are regarded as temporal filters for dynamic patters. The OPM is used to build local adaptive filters for a given nonstationary time series. Non-parametric confidence intervals are then computed for the residuals of the local models and used as decision thresholds for detecting novelties/anomalies. Computer simulations are carried out to compare the performances of the aforementioned algorithms.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/275 2019-06-05T12:56:49+00:00 Renato Fernandes Corrêa ojs.ub@uni-bielefeld.de Teresa Bernarda Ludermir ojs.ub@uni-bielefeld.de

This paper describes improving in Semantic Mapping, a feature extraction method useful to dimensionality reduction of vectors representing documents of large text collections. This method may be viewed as a specialization of the Random Mapping, method proposed in WEBSOM project. Semantic Mapping, Random Mapping and Principal Component Analysis (PCA) are applied to categorization of document collections using Self-Organizing Maps (SOM). Semantic Mapping generated document representation as good as PCA and much better than Random Mapping.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/276 2019-06-05T12:56:48+00:00 Patrick Letrémy ojs.ub@uni-bielefeld.de Marie Cottrell ojs.ub@uni-bielefeld.de Patrice Gaubert ojs.ub@uni-bielefeld.de Joseph Rynkiewicz ojs.ub@uni-bielefeld.de

The paper deals with the study of labor market dynamics, and aims to characterize its equilibriums and possible trajectories. The theoretical background is the theory of the segmented labor market. The main idea is that this theory is well adapted to interpret the observed trajectories, due to the heterogeneity of the work situations. The Kohonen algorithm is used to define the segments of the labor market. The trajectories are reconstructed by means of a non homogeneous Markov model and classified by using a Kohonen algorithm again.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/277 2019-06-05T12:56:47+00:00 Alfred Ultsch ojs.ub@uni-bielefeld.de

This paper sheds some light on the differences between SOM and emergent SOM (ESOM). The discussion in philosophy and epistemology about Emergence is summarized in the form of postulates. The properties of SOM are compared to these postulates. SOM fulfill most of the postulates. The epistemological postulates regarding this issue are hard, if not impossible, to prove. An alternative postulate relying on semiotic concepts, called "semiotic irreducibility" is proposed here. This concept is applied to U-Matrix on SOM with many neurons. This leads to the definition of ESOM as SOM producing a nontrivial U-Matrix on which the terms "watershed" and "catchment basin" are meaningful and which are cluster conform. The usefulness of the approach is demonstrated with an ESOM clustering algorithm which exploits the emergent properties of such SOM. Results on synthetic data also in blind studies are convincing. The application of ESOM clustering for a real world problem let to an excellent solution.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/278 2019-06-05T12:56:46+00:00 Miki Sirola ojs.ub@uni-bielefeld.de Golan Lampi ojs.ub@uni-bielefeld.de Jukka Parviainen ojs.ub@uni-bielefeld.de

Failure management in process industry has difficult tasks. Decision support in control rooms of nuclear power plants is needed. A prototype that uses Self-Organizing Map (SOM) method is under development in an industrial project. This paper has focus on failure detection and separation. A literature survey outlines the state-of-the-art and reflects our study to related works. Different SOM visualizations are used. Failure management scenarios are carried out to experiment the methodology and the Man-Machine Interface (MMI). U-matrix trajectory analysis and quantization error are discussed more in detail. The experiments show the usefulness of the chosen approach. Next step will be to add more practical views by analyzing real and simulated industrial data with the control room tool and by feedback from the end users.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/279 2019-06-05T12:56:45+00:00 Marc M. Van Hulle ojs.ub@uni-bielefeld.de

We develop a number of fixed point rules for training homogeneous, heteroscedastic but otherwise radially-symmetric Gaussian kernel-based topographic maps. We extend the batch map algorithm to the heteroscedastic case and introduce two candidates of fixed point rules for which the end-states, i.e., after the neighborhood range has vanished, are identical to the maximum likelihood Gaussian mixture modeling case. We compare their performance for clustering a number of real world data sets.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/280 2019-06-05T12:56:44+00:00 Arnaud De Decker ojs.ub@uni-bielefeld.de Gael De Lannoy ojs.ub@uni-bielefeld.de Michel Verleysen ojs.ub@uni-bielefeld.de

Functional data, often sampled at high frequency, lead to high-dimensional vectors. The curse of dimensionality makes the latter difficult to handle with standard data analysis tools. Functional data analysis tools take profit of the functional nature of data by projecting them on a smooth basis. This paper shows how to extend functional Self-Organizing Maps (SOM) to signal windows having different lengths. This technique may be applied for example on signal sampled regularly, but for which the duration of each signal is varying; an example concerns electrocardiography (ECG), where the signal is usually cut according to the variable period between two heart beats.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/281 2019-06-05T12:56:44+00:00 Philip Prentis ojs.ub@uni-bielefeld.de

This paper describes an image browsing and retrieval application called GalSOM. Bitmap images are described by their colour histograms and sorted using an improved variant of the tree-structured self-organising map (TS-SOM) algorithm. The advantages of using such a system are discussed in detail, and their application to the problem of image theft detection is proposed.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/282 2019-06-05T12:56:42+00:00 Christian Martin ojs.ub@uni-bielefeld.de Naryttza N. Diaz ojs.ub@uni-bielefeld.de Jörg Ontrup ojs.ub@uni-bielefeld.de Tim W. Nattkemper ojs.ub@uni-bielefeld.de

The advent of sequencing technologies allows to reassess the relationship between species in the hierarchically organized tree of life. Self-Organizing Maps (SOM) in Euclidean and hyperbolic space are applied to genomic signatures of 350 different organisms of the two superkingdoms Bacteria and Archaea to link the sequence signature space to pre-defined taxonomic levels, i.e. the tree of life. In the hyperbolic space the SOMs are trained by either the standard algorithm (HSOM) or in a hierarchical manner (H²SOM). For evaluating the SOM performances, distances between organisms in the feature space, on the SOM grid and in the taxonomy tree are compared pair-wise. We show that the structure recovered using the different SOMs reflects the gold standard of current taxonomy. The distances between species are better preserved when using the HSOM or H²SOM which makes the hyperbolic space better suited for embedding the high dimensional genomic signatures.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/283 2019-06-05T12:56:41+00:00 Abderrahmane Boubezoul ojs.ub@uni-bielefeld.de Sébastien Paris ojs.ub@uni-bielefeld.de Mustapha Ouladsine ojs.ub@uni-bielefeld.de

This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/284 2019-06-05T12:56:40+00:00 J. R. G. Pulido ojs.ub@uni-bielefeld.de S. B. F. Flores ojs.ub@uni-bielefeld.de P. D. Reyes ojs.ub@uni-bielefeld.de R. A. Diaz ojs.ub@uni-bielefeld.de J. J. C. Castillo ojs.ub@uni-bielefeld.de

This paper describes an approach we have been using to identify specific-domain ontology components by using Self-Organizing Maps. These components are clustered together in a natural way according to their similarity. The knowledge maps, as we call them, show colored regions containing knowledge components that may be used to populate an specific-domain ontology. Later, these ontology may be used by software agents to carry out basic reasoning task on our behalf. In particular, we deal with the issue of not constructing the ontology from scratch, our approach helps us to speed up the ontology creation process.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/285 2019-06-05T12:56:40+00:00 Jochen J. Steil ojs.ub@uni-bielefeld.de Alessandro Sperduti ojs.ub@uni-bielefeld.de

Self-Organizing Maps for Structures (SOM-SD) are neural networks models capable of processing structured data, such as sequences and trees. The evaluation of the encoding quality achieved by these maps should neither be measured only by the quantization error as in the standard SOM, which fails to capture the structural aspects, nor by other topology preserving indexes which are ill-defined for discrete structures. We propose new indexes for the evaluation of encoding quality which are customized to the structural nature of input data. These indexes are used to evaluate the quality of SOM-SDs trained on a benchmark dataset introduced earlier in. We show that the proposed indexes capture relevant structural features of the tree encoding additional to the statistical features of the training data labels.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/286 2019-06-05T12:56:39+00:00 Marina Resta ojs.ub@uni-bielefeld.de

Working with five minutes data, we have studied a number of trading rules based on the responses of Kohonen's Self Organizing Maps, evaluating the results with both financial and statistical indicators, as well as by comparison with classical buy and hold strategy. At the current stage our major findings may be summarized as follows: a) Kohonen's maps are helpful to localize profitable intraday patterns, and b) they generally make possible to achieve higher performances than common buy and hold strategy.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/287 2019-06-05T12:56:07+00:00 Lutz Herrmann ojs.ub@uni-bielefeld.de Alfred Ultsch ojs.ub@uni-bielefeld.de

Semi-supervised learning aims at discovering spatial structures in high-dimensional input spaces when insufficient background information about clusters is available. A particulary interesting approach is based on propagation of class labels through proximity graphs. The Self-Organizing Map itself can be seen as such a proximity graph that is suitable for label propagation. It turns out that Zhu's popular label propagation method can be regarded as a modification of the SOM's well known batch learning rule. In this paper, an approach for semi-supervised learning is presented. It is based on label propagation in trained Self-Organizing Maps. Furthermore, a simple yet powerful method for crucial parameter estimation is presented. The resulting clustering algorithm is tested on the fundamental clustering problem suite (FCPS).

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/288 2019-06-05T12:56:06+00:00 Aree Witoelar ojs.ub@uni-bielefeld.de Michael Biehl ojs.ub@uni-bielefeld.de Barbara Hammer ojs.ub@uni-bielefeld.de

Learning Vector Quantization (LVQ) are popular multi-class classification algorithms. Prototypes in an LVQ system represent the typical features of classes in the data. Frequently multiple prototypes are employed for a class to improve the representation of variations within the class and the generalization ability. In this paper, we investigate the dynamics of LVQ in an exact mathematical way, aiming at understanding the influence of the number of prototypes and their assignment to classes. The theory of on-line learning allows a mathematical description of the learning dynamics in model situations. We demonstrate using a system of three prototypes the different behaviors of LVQ systems of multiple prototype and single prototype class representation.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/289 2019-06-05T12:56:05+00:00 Aluizio R. F. Araújo ojs.ub@uni-bielefeld.de Diogo C. Costa ojs.ub@uni-bielefeld.de

A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called Local Adaptive Receptive Field Self-Organizing Map (LARFSOM-RBF), is a two-stage network capable of both color and border segment images. The color segmentation stage is responsibility of LARFSOM which is characterized by adaptive number of nodes, fast convergence and variable topology. For border segmentation RBF nodes are included to determine the border pixels using previously learned information of LARFSOM. LARFSOM-RBF was tested to segment images with different degrees of complexity showing promising results.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/290 2019-06-05T12:56:04+00:00 Hiroshi Dozono ojs.ub@uni-bielefeld.de Takeshi Takahashi ojs.ub@uni-bielefeld.de

In this paper, we introduce an algorithm of Self-Organizing Maps(SOM) which can map the genome sequence continuously on the map. The DNA sequences are considered to have the special features depending on the regions where the sequences are taken from or the gene functions of the proteins which are translated from the sequences. If the hidden features of the DNA sequences are extracted from the DNA sequences, they can be used for predicting the regions or the functions of the sequences. In this paper, we propose the algorithms using two stage SOM which organizes the sequences of the specific length at the first stage and organizes the set of sequences at the 2nd stage This algorithm can map the genome sequences on the map at each stage depending on the features of the sequences. We made some analyses of the genome sequences concerning the functions, species and secondary structure of the sequences.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/291 2019-06-05T12:56:03+00:00 Tetsuo Furukawa ojs.ub@uni-bielefeld.de

In this paper, two generalizations of the SOM are introduced. The first of these extends the SOM to deal with more generalized classes of objects besides the vector dataset. This generalization is realized by employing modular networks instead of reference vector units and is thus called a modular network SOM (mnSOM). The second generalization involves the extension of the SOM from "map" to "homotopy", allowing the SOM to deal with a set of data distributions rather than a set of data vectors. The resulting architecture is called SOM^n, where each reference unit represents a tensor of rank n. These generalizations are expected to provide good platforms on which to build brain-like intelligence.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/292 2019-06-05T12:56:02+00:00 Michael Bui ojs.ub@uni-bielefeld.de Masahiro Takatsuka ojs.ub@uni-bielefeld.de

Data visualization has become an important tool for analyzing very complex data. In particular, spatial visualization enables users to view data in a intuitive manner. It has typically been used to externalize clusters and their relationships which exist in highly complex multidimensional data. We envisage that not only cluster formation and relationships but also other types of information, such as temporal changes of datum, can be extracted through the spatialization. In this paper, we investigate an application of trajectory/path analysis carried out using a Self-Organizing Map as a spatialization method. We propose an application of distance transformations to the Geodesic Self-Organizing Map. This new approach allows a user to visually inspect the trajectory of multidimensional knowledge pieces on a two-dimensional space. The trajectories discovered through this approach are essentially the shortest paths between two points on the Self-Organizing Map. However, those paths might go outside of the input dataspace due to the connectivity of neurons imposed by the grid structure. We also present a method to find the shortest path, which falls within the input dataspace using simple floodplain analysis.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/293 2019-06-05T12:56:01+00:00 Nils T. Siebel ojs.ub@uni-bielefeld.de Jochen Krause ojs.ub@uni-bielefeld.de Gerald Sommer ojs.ub@uni-bielefeld.de

In this article we present EANT, "Evolutionary Acquisition of Neural Topologies", a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES. EANT can create NNs that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different one, called NEAT, "NeuroEvolution of Augmenting Topologies", to create networks that control a robot in a visual serving scenario.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/294 2019-06-05T12:56:01+00:00 Mikaela Klami ojs.ub@uni-bielefeld.de Timo Honkela ojs.ub@uni-bielefeld.de

We present the results of an analysis of a text corpus of 129,000 abstracts of NSF-sponsored basic research projects between years 1990 and 2003. The methods used in the analysis include term extraction based on a reference corpus and an entropy measure, and the Self-Organizing Map algorithm for the formation of a term map and a document map. Methodologically, the basic approach is based on earlier developments, such as word category maps and the WEBSOM method, but in the level of details, we report several new aspects and quantitative comparison results between methodological variants in this article. The data covers a quite large proportion of US-based scientific research during recent years. The analysis results indicate the basic patterns discernable in the data, both at the level of the awards and at the terminology used in them.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/295 2019-06-05T12:56:00+00:00 Tetsuo Furukawa ojs.ub@uni-bielefeld.de

In this paper, we propose a conceptual learning algorithm called the 'self-organizing homotopy (SOH)' together with an implementation thereof. As in the case of the SOM, our SOH organizes a homotopy in a self-organizing manner by giving a set of data episodes. Thus it is an extension of the SOM, moving from a 'map' to a 'homotopy'. From a geometrical viewpoint, the SOH represents a set of (i.e. multiple) data distributions by a fiber bundle, whereas the SOM represents a single data distribution by a manifold. One of the solutions to the SOH is SOM², in which every reference vector unit of the conventional SOM is itself replaced by an SOM. Consequently SOM² has the ability to represent a fiber bundle, i.e. a product manifold, by using a product space of SOM x SOM. It is expected that SOHs will play important roles in the fields of pattern recognition, adaptive functions, context understanding, and others, in which nonlinear manifolds and the homotopy play crucial roles.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/296 2019-06-05T12:55:59+00:00 Haruna Matsushita ojs.ub@uni-bielefeld.de Yoshifumi Nishio ojs.ub@uni-bielefeld.de

In the real world, it is not always true that the nextdoor house is close to my house, in other words, "neighbors" are not always "true neighbors". In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain the more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/297 2019-06-05T12:55:58+00:00 Nikolaos Tsimboukakis ojs.ub@uni-bielefeld.de George Tambouratzis ojs.ub@uni-bielefeld.de

In this paper, a novel SOM-based system for document organization is presented. The purpose of the system is the classification of a document collection in terms of document content. The system possesses a two-level hybrid connectionist architecture that comprises (i) an automatically created word map using a SOM, which functions as a feature extraction module and (ii) a supervised MLP-based classifier, which provides the final classification result. The experiments, which have been performed on Modern Greek text documents, indicate that the proposed system separates effectively the different types of text.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/298 2019-06-05T12:55:57+00:00 Nikolai Alex ojs.ub@uni-bielefeld.de Barbara Hammer ojs.ub@uni-bielefeld.de Frank Klawonn ojs.ub@uni-bielefeld.de

The presence of very large data sets poses new problems to standard neural clustering and visualization algorithms such as Neural Gas (NG) and the Self-Organizing-Map (SOM) due to memory and time constraints. In such situations, it is no longer possible to store all data points in the main memory at once and only a few, ideally only one run over the whole data set is still affordable to achieve a feasible training time. In this contribution we propose single pass extensions of the classical clustering algorithms NG and fuzzy-k-means which are based on a simple patch decomposition of the data set and fast batch optimization schemes of the respective cost function. The algorithms maintain the benefits of the original ones including easy implementation and interpretation as well as large flexibility and adaptability because of the underlying cost function. We demonstrate the efficiency of the approach in a variety of experiments.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/299 2019-06-05T12:55:56+00:00 Pablo A. Estévez ojs.ub@uni-bielefeld.de Ricardo Zilleruelo-Ramos ojs.ub@uni-bielefeld.de Rodrigo Hernández ojs.ub@uni-bielefeld.de Leonardo Causa ojs.ub@uni-bielefeld.de Claudio M. Held ojs.ub@uni-bielefeld.de

In this paper the Merge Neural Gas (MNG) model is applied to detect sleep spindles in EEG. Features are extracted from windows of the EEG by using short time Fourier transform. The total power spectrum is computed in six frequency bands and used as input to the MNG network. The results show that MNG outperforms simple neural gas in correctly detecting sleep spindles. In addition the temporal quantization results as well as sleep trajectories are visualized on two-dimensional maps by using the OVING projection method.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/300 2019-06-05T12:55:55+00:00 Jan Frederik Steffen ojs.ub@uni-bielefeld.de Robert Haschke ojs.ub@uni-bielefeld.de Helge Ritter ojs.ub@uni-bielefeld.de

We present an approach to dextrous robot grasping which combines a purely tactile-driven algorithm with an implicit representation of grasp experience to yield an algorithm which can handle arbitrary, partially unknown grasp situations. During the grasp movement, the obtained contact information is used to dynamically adapt the grasping control by targeting the best matching posture from the experience base. Thus, the robot recalls and actuates a grasp it already successfully performed in a similar tactile context. To efficiently represent the experience, we introduce the Grasp Manifold assuming that grasp postures form a smooth manifold in hand posture space. We present a simple way of providing approximations of Grasp Manifolds using Self-Organising Maps (SOMs) and study the properties of the represented grasp manifolds concerning their smoothness and robustness against clustered training data.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/301 2019-06-05T12:55:54+00:00 Alexandra Scherbart ojs.ub@uni-bielefeld.de Wiebke Timm ojs.ub@uni-bielefeld.de Sebastian Böcker ojs.ub@uni-bielefeld.de Tim W. Nattkemper ojs.ub@uni-bielefeld.de

In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type based on Self-Organizing Maps (SOMs) for peptide prototyping and learning locally tuned regression functions for peak intensity prediction in MALDI-TOF mass spectra. We obtain results comparable to those obtained by nu-Support Vector Regression and show how the SOM learning architecture provides a basis for peptide feature profiling and visualisation.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/302 2019-06-05T12:55:53+00:00 Lei Shi ojs.ub@uni-bielefeld.de Dingsheng Luo ojs.ub@uni-bielefeld.de Lei Xu ojs.ub@uni-bielefeld.de

Local Factor Analysis (LFA) is known as more general and powerful than Gaussian Mixture Model (GMM) in unsupervised learning with local subspace structure analysis. In the literature of text-independent speaker identification, GMM has been widely used and investigated, with some preprocessing or postprocessing approaches, while there still lacks efforts on LFA for this task. In pursuit of fast implementation for LFA modeling, this paper focuses on the Bayesian Ying-Yang automatic learning with data smoothing based regularization (BYY-A), which makes automatic model selection during parameter learning. Furthermore for sequence classification, based on trained LFA models, we design and analyze a three-level combination, namely sequence, classifier and committee, respectively. Different combination approaches are designed with variant sequential topologies and voting schemes. Experimental results on the KING speech corpus demonstrate the proposed approaches' effectiveness and potentials.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/303 2019-06-05T12:55:52+00:00 Muhammad Aziz Muslim ojs.ub@uni-bielefeld.de Masumi Ishikawa ojs.ub@uni-bielefeld.de Tetsuo Furukawa ojs.ub@uni-bielefeld.de

Our previous studies assigned labels to mnSOM modules based on the assumption that winner modules corresponding to subsequences in the same class share the same label. We propose segmentation using hierarchical clustering based on the resulting mnSOM. Since it does not need the above unrealistic assumption, it gains practical importance at the sacrifice of the deterioration of the segmentation performance by 1.2%. We compare the performance of task segmentation for two kinds of module architecture in mnSOM. The result is that module architecture with sensory-motor signals as target outputs has superior performance to that with only sensory signals as target outputs.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/304 2019-06-05T12:55:51+00:00 Antonio Neme ojs.ub@uni-bielefeld.de Pedro Miramontes ojs.ub@uni-bielefeld.de

In the self-organizing map (SOM), the best matching units (BMUs) affect neurons as a function of distance and the learning parameter. Here we study the effects in SOM when a new parameter in the learning rule, the activation frequency, is included. This parameter is based on the relative frequency by which each neuron is included in each BMU's neighborhood, so there is an individual memory (synapse strength) of the activation received from each neuron. The parameter leads to non-radial influence areas for BMUs, what is a more realistic feature observed in the brain cortex which modifies the map formation dynamics, including the fact that the weight vector for BMU may not be the closest one to the input stimulus after weight adaptation. Also, two error measures are lower for the maps trained with this model than those obtained with SOM, as shown in several experiments with six data sets.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/305 2019-06-05T12:55:50+00:00 Kristian Nybo ojs.ub@uni-bielefeld.de Jarkko Venna ojs.ub@uni-bielefeld.de Samuel Kaski ojs.ub@uni-bielefeld.de

We have recently introduced rigorous goodness criteria for information visualization by posing it as a visual neighbor retrieval problem, where the task is to find proximate high-dimensional data based only on a low-dimensional display. Standard information retrieval criteria such as precision and recall can then be used for information visualization. We introduced an algorithm, Neighbor Retrieval Visualizer (NeRV), to optimize the total cost of retrieval errors. NeRV was shown to outperform alternative methods, but the SOM was not included in the comparison. In empirical experiments of this paper the SOM turns out to be comparable to the best methods in terms of (smoothed) precision but not on recall. On a related measure called trustworthiness, the SOM outperforms all others. Finally, we suggest that for information visualization tasks the free parameters of the SOM could be optimized for information visualization with cross-validation.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/306 2019-06-05T12:55:49+00:00 Barbara Hammer ojs.ub@uni-bielefeld.de Alexander Hasenfuß ojs.ub@uni-bielefeld.de Fabrice Rossi ojs.ub@uni-bielefeld.de Marc Strickert ojs.ub@uni-bielefeld.de

Recently, batch optimization schemes of the self-organizing map and neural gas have been modified to allow arbitrary distance measures.This principle is particularly suitable for complex applications where data are compared by means of problem-specific, possibly discrete metrics such as protein sequences. However, median variants do not allow a continuous update of prototype locations and their capacity is thus restricted. In this contribution, we consider the relational dual of batch optimization which can be formulated in terms of pairwise distances only such that an application to arbitrary distance matrices becomes possible. For SOM, a direct visualization of data is given by means of the underlying (euclidean or hyperbolic) lattice structure. For NG, pairwise distances of prototypes can be computed based on a given data matrix only, such that subsequent mapping by means of multidimensional scaling can be applied.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/307 2019-06-05T12:55:48+00:00 Dietlind Zühlke ojs.ub@uni-bielefeld.de

A network model is introduced that allows a multimodal registration of two images. It can be used for a image-model or a model-model registration. The application of the network to registering tomographic to 3D ultrasonic data is introduced. Results on artificial and real ultrasound image data sets are discussed.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/308 2019-06-05T12:55:47+00:00 Atsushi Shimada ojs.ub@uni-bielefeld.de Rin-Ichiro Taniguchi ojs.ub@uni-bielefeld.de

We propose a new incremental learning method of Self-Organizing Map. Basically, there are three problems in the incremental learning of Self-Organizing Map: 1. depletion of neurons, 2. oblivion of training data previously given, 3. destruction of topological relationship among training samples. Weight-fixed neurons and weight-quasi-fixed neurons are very effective for the second problem. However the other problems still remain. Therefore, we improve the incremental learning method with weight-fixed neurons and weight-quasi-fixed neurons. We solve the problems by introducing a mechanism to increase the number of neurons effectively in the incremental learning process.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/309 2019-06-05T12:55:46+00:00 Dietlind Zühlke ojs.ub@uni-bielefeld.de

A network model is introduced that allows the extraction of the topological structure of a set of input vectors corresponding to image voxels from a 3D doppler or contrast enhanced ultrasound. This extraction is a precondition for many medical image registration algorithms. Results on artificial and real ultrasound image data sets are discussed.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/310 2019-06-05T12:55:45+00:00 Jorma Laaksonen ojs.ub@uni-bielefeld.de Markus Koskela ojs.ub@uni-bielefeld.de Mats Sjöberg ojs.ub@uni-bielefeld.de Ville Viitaniemi ojs.ub@uni-bielefeld.de Hannes Muurinen ojs.ub@uni-bielefeld.de

Video summarization is a process where a long video file is converted to a considerably shorter form. The video summary can then be used to facilitate efficient searching and browsing of video files in large video collections. The aim of successful automatic summarization is to preserve as much as possible from the essential content of each video. What is essential is of course subjective and also dependent on the use of the videos and the overall content of the collection. In this paper we present an overview of the SOM-based methodology we have used for video summarization, which analyzes the temporal trajectories of the best-matching units of frame-wise feature vectors. It has been developed as a part of PicSOM, our content-based multimedia information retrieval and analysis framework. The video material we have used in our experiments comes from NIST's annual TRECVID evaluation for content-based video retrieval systems.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings https://biecoll2.ub.uni-bielefeld.de/index.php/wsom/article/view/311 2019-06-05T12:55:45+00:00 Sebastian Risi ojs.ub@uni-bielefeld.de Fabian Mörchen ojs.ub@uni-bielefeld.de Alfred Ultsch ojs.ub@uni-bielefeld.de Pascal Lehwark ojs.ub@uni-bielefeld.de

Different methods of organizing large collections of music with databionic mining techniques are described. The Emergent Self-Organizing Map is used to cluster and visualize similar artists and songs. The first method is the MusicMiner system that utilizes semantic descriptions learned from low level audio features for each song. The second method uses tags that have been assigned to music artists by the users of the social music platform Last.fm. For both methods we demonstrate the visualization capabilities of the U-Map. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The semantic concepts behind the features enhance the interpretability of the maps.

2007-12-31T00:00:00+00:00 Copyright (c) 2023 International Workshop on Self-Organizing Maps : Proceedings