Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4639687 | Journal of Computational and Applied Mathematics | 2012 | 11 Pages |
Abstract
Blind source separation (BSS) is an increasingly popular data analysis technique with many applications. Several methods for BSS using the statistical properties of original sources have been proposed; for a famous case, non-Gaussianity, this leads to independent component analysis (ICA). In this paper, we propose a hybrid BSS method based on linear and nonlinear complexity pursuit, which combines three statistical properties of source signals: non-Gaussianity, linear predictability and nonlinear predictability. A gradient learning algorithm is presented by minimizing a loss function. Simulations verify the efficient implementation of the proposed method.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Zhenwei Shi, Hongjuan Zhang, Zhiguo Jiang,