The self-organizing map as a visual neighbor retrieval method

Abstract

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.