A globally convergent learning algorithm for PCA neural networks

  • Authors:

  • Mao Ye;Zhang Yi;JianCheng Lv

  • Affiliations:

  • University of Electronic Science and Technology of China, CI Lab, School of Computer Science and Engineering, 610054, Chengdu, China;University of Electronic Science and Technology of China, CI Lab, School of Computer Science and Engineering, 610054, Chengdu, China;University of Electronic Science and Technology of China, CI Lab, School of Computer Science and Engineering, 610054, Chengdu, China

  • Venue:
  • Neural Computing and Applications
  • Year:
  • 2005

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Abstract

Principal component analysis (PCA) by neural networks is one of the most frequently used feature extracting methods. To process huge data sets, many learning algorithms based on neural networks for PCA have been proposed. However, traditional algorithms are not globally convergent. In this paper, a new PCA learning algorithm based on cascade recursive least square (CRLS) neural network is proposed. This algorithm can guarantee the network weight vector converges to an eigenvector associated with the largest eigenvalue of the input covariance matrix globally. A rigorous mathematical proof is given. Simulation results show the effectiveness of the algorithm.