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Growing Hierarchical Principal Components Analysis Self-Organizing Map

  • Stones Lei Zhang
  • Zhang Yi
  • Jian Cheng Lv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

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.

Keywords

Covariance Matrix Vector Basis Training Process Machine Learn Database Orthonormal Vector Basis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stones Lei Zhang
    • 1
  • Zhang Yi
    • 1
  • Jian Cheng Lv
    • 1
  1. 1.Computational Intelligence Laboratory, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduP.R. China

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