Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653376 | Neurocomputing | 2005 | 9 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Xiaolong Zhu, Jimin Ye, Xianda Zhang,