Letters: A fixed-point nonlinear PCA algorithm for blind source separation

Authors:
Xiaolong Zhu;Jimin Ye;Xianda Zhang
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, China;Key Lab for Radar Signal Processing, Xidian University, Xi'an 710071, China;Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Neurocomputing
Year:
2005

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Abstract

This paper addresses the problem of blind source separation and presents a fixed-point nonlinear principal component analysis (NPCA) algorithm. It is a block-wise batch algorithm and gives an alternative perspective on existing adaptive online NPCA algorithms. Utilizing new activation functions that automatically satisfy a stability condition, the proposed algorithm can separate mixed signals with sub- and super-Gaussian source distributions. The efficiency is confirmed by extensive computer simulations on man-made sources as well as practical speech signals.