Growing hierarchical principal components analysis self-organizing map

Authors:
Stones Lei Zhang;Zhang Yi;Jian Cheng Lv
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

In this paper, we propose a new self-growing hierarchical principal components analysis self-organizing neural networks model. This dynamically growing model expands the ability of the PCASOM model that represents the hierarchical structure of the input data. It overcomes the shortcoming of the PCASOM model in which the fixed the network architecture must be defined prior to training. Experiment results showed that the proposed model has better performance in the tradition clustering problem.