The self-organizing map as a visual information retrieval method
Kristian Nybo, Jarkko Venna and Samuel Kaski
In: Proceedings of WSOM'07, 6th International Workshop on Self-Organizing Maps (2007) Bielefeld University , Bielefeld, Germany .

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, and 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 comparisons did not include one of the methods widely used for information visualization, namely the Self-Organizing Map (SOM). In empirical experiments of this paper the SOM turns out to be comparable to the best methods in terms of smoothed precision, but not in terms of 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 with cross-validation to maximize its visual information retrieval performance. This would remove the need to choose the size of the SOM grid and the final radius by rules of thumb.

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