A fixed-point nonlinear PCA algorithm for blind source separation

doi:10.1016/j.neucom.2005.05.009

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A fixed-point nonlinear PCA algorithm for blind source separation

2005-12-01

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DOI：10.1016/j.neucom.2005.05.009

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Abstract

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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. (c) 2005 Elsevier B.V. All rights reserved.

Keywords:

blind source separation

nonlinear principal component analysis

independent component analysis

fixed-point algorithm

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