DocumentCode
1302036
Title
Blind source separation using clustering-based multivariate density estimation algorithm
Author
He, Zhenya ; Yang, Luxi ; Liu, Ju ; Lu, Ziyi ; He, Chen ; Shi, Yuhui
Author_Institution
Dept. of Radio Eng., Southeast Univ., Nanjing, China
Volume
48
Issue
2
fYear
2000
fDate
2/1/2000 12:00:00 AM
Firstpage
575
Lastpage
579
Abstract
A learning algorithm is developed for blind separation of the independent source signals from their linear mixtures. The algorithm is based on minimizing a contrast function defined in terms of the Kullback-Leibler distance. We use a clustering-based multivariate density estimation approach to reduce the number of the parameters to be updated. Simulations illustrate the validity of the algorithm
Keywords
estimation theory; image processing; minimisation; pattern clustering; Kullback-Leibler distance; blind source separation; clustering-based multivariate density estimation algorithm; contrast function; images; independent source signals; learning algorithm; linear mixture; Blind equalizers; Blind source separation; Channel estimation; Clustering algorithms; Deconvolution; Digital signal processing; Helium; Independent component analysis; Signal processing algorithms; Source separation;
fLanguage
English
Journal_Title
Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1053-587X
Type
jour
DOI
10.1109/78.823988
Filename
823988
Link To Document