A neural network learning algorithm for adaptive principal component extraction (APEX)

S. Y. Kung, K. I. Diamantaras

Research output: Contribution to journalConference articlepeer-review

122 Scopus citations


The problem of the recursive computation of the principal components of a vector stochastic process is discussed. The applications of this problem arise in modeling of control systems, high-resolution spectrum analysis, image data compression, motion estimation, etc. An algorithm called APEX which can recursively compute the principal components using a linear neural network is proposed. The algorithm is recursive and adaptive: given the first m-1 principal components, it can produce the mth component iteratively. The numerical theoretical basis of the fast convergence of the APEX algorithm is given, and its computational advantages over previously proposed methods are demonstrated. Extension to extracting constrained principal components using APEX is also discussed.

Original languageEnglish (US)
Pages (from-to)861-864
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1990
Event1990 International Conference on Acoustics, Speech, and Signal Processing: Speech Processing 2, VLSI, Audio and Electroacoustics Part 2 (of 5) - Albuquerque, New Mexico, USA
Duration: Apr 3 1990Apr 6 1990

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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