Letters: A fast NPCA algorithm for online blind source separation

Authors:
Xiaolong Zhu;Xianda Zhang;Yongtao Su
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Neurocomputing
Year:
2006

Citing 7
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Abstract

This paper addresses the problem of blind source separation (BSS) and presents an optimum step size which makes the nonlinear principal component analysis (NPCA) cost function descend in the fastest way. By applying this step size in the self-stabilized NPCA algorithm, a fast NPCA algorithm is obtained. Computer simulations of online BSS show that the new algorithm works more efficiently than the existing least-mean-square (LMS)-type and recursive least-squares (RLS)-type NPCA algorithms.