An on-line unsupervised learning machine for adaptive feature extraction

Author

Chen, Hong ; Liu, Ruey-Wen

Author_Institution

Dept. of Electr. Eng., Notre Dame Univ., IN, USA

fYear

1993

fDate

3-6 May 1993

Firstpage

535

Abstract

An on-line unsupervised learning machine (LEAP) for adaptive feature extraction (principal component decomposition of a stochastic process) is introduced. Some results on convergence analysis of the LEAP system are summarized. A result on the identification of a nontrivial domain of attraction for the system is given. A new application is shown. LEAP can be extended to perform on-line higher-order statistics component decomposition of a stochastic process. This extended system (ELEAP) can then be combined with two identification techniques resulting in an on-line signal processing system for parameter estimation of multichannel moving-average processes using higher-order statistics

Keywords

adaptive estimation; feature extraction; higher order statistics; image recognition; moving average processes; parameter estimation; unsupervised learning; LEAP system; adaptive feature extraction; convergence analysis; higher-order statistics; identification techniques; multichannel moving-average processes; nontrivial domain of attraction; parameter estimation; principal component decomposition; stochastic process; unsupervised learning machine; Computer networks; Feature extraction; Higher order statistics; Machine learning; Neurons; Principal component analysis; Signal processing; Signal processing algorithms; Stochastic processes; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

Print_ISBN

0-7803-1281-3

Type

conf

DOI

10.1109/ISCAS.1993.393776

Filename

393776