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
fDate :
2/1/2000 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on
