Title :
Temporal Bayesian Ying-Yang dependence reduction, blind source separation and principal independent components
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Abstract :
A general recursive temporal BYY dependence reduction system, previously proposed by the author (1998), has been further re-elaborated systematically. Three new developments have been also made. A temporal independent component analysis (ICA) algorithm is proposed for separating blind sources from observations without sensor noise. The other two studies are for solving blind source separation problems with sensor noise of binary and real temporal sources, respectively. Moreover, the problem of how to determine principal independent components is addressed with criteria provided
Keywords :
Bayes methods; neural nets; noise; principal component analysis; signal resolution; temporal reasoning; ICA; TBYY dependence reduction; binary temporal sources; blind source separation; principal independent components; real temporal sources; recursive temporal BYY dependence reduction system; sensor noise; temporal Bayesian ying-yang dependence reduction; temporal independent component analysis; Algorithm design and analysis; Bayesian methods; Blind source separation; Computer science; Independent component analysis; Neural networks; Principal component analysis; Signal processing; Signal processing algorithms; Source separation;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831104
