An unsupervised neural model for oriented principal component extraction

K. I. Diamantaras, S. Y. Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The concept of oriented principal component (OPC) analysis is introduced. It is the extension of the GSVD (generalized singular value decomposition) concept to the case of random processes (much like principal component analysis extends SVD for stochastic signals). In the random signal case, OPC analysis is equivalent to matched filtering and can be found useful in many classification and detection applications. The authors propose a corresponding neural model equipped with an efficient training algorithm for estimating the oriented principal component of two stochastic processes without assuming explicit knowledge of their statistics. The algorithm is based on the (normalized) learning rule proposed by Hebb for training the synaptic weights of a network of neurons. Both the theoretical justification and the numerical performance are shown, giving an explicit estimate of the learning rate parameter for best convergence speed.

Original languageEnglish (US)
Title of host publicationProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherPubl by IEEE
Pages1049-1052
Number of pages4
ISBN (Print)0780300033
DOIs
StatePublished - Jan 1 1991
EventProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 - Toronto, Ont, Can
Duration: May 14 1991May 17 1991

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume2
ISSN (Print)0736-7791

Other

OtherProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91
CityToronto, Ont, Can
Period5/14/915/17/91

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Diamantaras, K. I., & Kung, S. Y. (1991). An unsupervised neural model for oriented principal component extraction. In Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1049-1052). (Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing; Vol. 2). Publ by IEEE. https://doi.org/10.1109/icassp.1991.150528