DocumentCode
1393942
Title
Recursive least squares approach to combining principal and minor component analyses
Author
Wong, Arnold-Shu-Yan ; Wong, Kwok-Wo ; Leung, Chi-sing
Author_Institution
Dept. of Electron. Eng., City Polytech. of Hong Kong, Kowloon, Hong Kong
Volume
34
Issue
11
fYear
1998
fDate
5/28/1998 12:00:00 AM
Firstpage
1074
Lastpage
1076
Abstract
A novel approach for high-performance data compression using neural networks is proposed. After the principal components of the input vectors are extracted, the error covariance matrix obtained in the recursive least square training process is used to perform minor components pruning so that a higher compression ratio is achieved. Simulation results show that our method effectively combines principal and minor component analyses
Keywords
covariance matrices; data compression; image coding; image reconstruction; least squares approximations; neural nets; compression ratio; data compression; error covariance matrix; input vectors; minor component analyses; minor components pruning; neural networks; principal component analyses; recursive least squares approach; training process;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:19980765
Filename
684025
Link To Document