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RBFSOM: An Efficient Algorithm for LargeScale MultiSystem Learning
Takashi OHKUBO Kazuhiro TOKUNAGA Tetsuo FURUKAWA
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E92D
No.7
pp.13881396 Publication Date: 2009/07/01
Online ISSN: 17451361
DOI: 10.1587/transinf.E92.D.1388
Print ISSN: 09168532 Type of Manuscript: Special Section PAPER (Special Section on Large Scale Algorithms for Learning and Optimization) Category: Keyword: selforganizing map, SOM, modular network SOM, mnSOM, SOM of SOMs,
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Summary:
This paper presents an efficient algorithm for largescale multisystem learning task. The proposed architecture, referred to as the 'RBF×SOM', is based on the SOM^{2}, that is, a'SOM of SOMs'. As is the case in the modular network SOM (mnSOM) with multilayer perceptron modules (MLPmnSOM), the aim of the RBF×SOM is to organize a continuous map of nonlinear functions representing multiclass inputoutput relations of the given datasets. By adopting the algorithm for the SOM^{2}, the RBF×SOM generates a map much faster than the original mnSOM, and without the local minima problem. In addition, the RBF×SOM can be applied to more difficult cases, that were not easily dealt with by the MLPmnSOM. Thus, the RBF×SOM can deal with cases in which the probability density of the inputs is dependent on the classes. This tends to happen more often as the input dimension increases. The RBF×SOM therefore, overcomes many of the problems inherent in the MLPmnSOM, and this is crucial for application to large scale tasks. Simulation results with artificial datasets and a meteorological dataset confirm the performance of the RBF×SOM.

