Using recursive least square learning method for principal and minor components analysis

Author

Wong, A.S.Y. ; Wong, K.W. ; Leung, C.S.

Author_Institution

Dept. of Electr. Electron., Hong Kong Univ., Hong Kong

Volume

2

fYear

1998

fDate

12-15 May 1998

Firstpage

1089

Abstract

In combining principal and minor components analysis, a parallel extraction method based on the recursive least square algorithm is suggested to extract the principal components of the input vectors. After the extraction, the error covariance matrix obtained in the learning process is used to perform minor components analysis. The minor components found are then pruned so as to achieve a higher compression ratio. Simulation results show that both the convergent speed and the compression ratio are improved, which in turn indicate that our method effectively combines the extraction of the principal components and the pruning of the minor components

Keywords

convergence of numerical methods; covariance matrices; data compression; feature extraction; image reconstruction; learning (artificial intelligence); least squares approximations; parallel processing; recursive estimation; compression ratio; convergent speed; error covariance matrix; feedforward neural networks; image reconstruction; input vectors; learning process; minor components analysis; minor components pruning; parallel extraction method; principal components analysis; recursive least square algorithm; recursive least square learning method; simulation results; Computational complexity; Convergence; Covariance matrix; Equations; Learning systems; Least squares methods; Neural networks; Neurons; Principal component analysis; Resonance light scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.675458

Filename

675458