Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
531448 | Pattern Recognition | 2009 | 10 Pages |
Abstract
Independent component analysis (ICA) and blind source separation (BSS) methods have been used for pattern recognition problems. It is well known that ICA and BSS depend on the statistical properties of original sources or components, such as non-Gaussianity. In the paper, using a statistical property—nonlinear autocorrelation and maximizing the nonlinear autocorrelation of source signals, we propose a fast fixed-point algorithm for BSS. We study its convergence property and show that its convergence speed is at least quadratic. Simulations by the artificial signals and the real-world applications verify the efficient implementation of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Zhenwei Shi, Changshui Zhang,