Network Component Analysis for Blind Source Separation
Blind source separation has found applications in various areas including biomedical signal processing and genomic signal processing. Often, blind source separation is solved via independent component analysis (ICA) by assuming and utilizing mutual independence among source signals.However, in bio-signal and genomic signal processing, the assumption of independence is often untrue, and the performance of the ICA approach is not as good. Much effort has been devoted to searching alternative approaches to blind source separation without the independence assumption. One idea known as network component analysis (NCA) is developed to identify the underlying regulatory signals of transcription factors in the gene regulatory network. In this paper we show that NCA is a general method for blind source separation using a priori information on the mixing matrix. An alternative proof of identifiability using NCA is proposed and a novel method to solve the problem is developed. Validation is made through computer simulations.
C.Q.Chang Y.S.Hung P.C.W.Fung Z.Ding
Department of Electrical & Electronic Engineering The University of Hong Kong Pokfulam Road, Hong Ko Department of Electrical & Computer Engineering University of California Davis, CA95616, USA
国际会议
2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)
广西桂林
英文
323-326
2006-06-25（万方平台首次上网日期，不代表论文的发表时间）