Hybrid linear and nonlinear complexity pursuit for blind source separation

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:236

Article

Shi, Zhenwei¹  Zhang, Hongjuan²  Jiang, Zhiguo¹ 

[1] Beihang Univ, Sch Astronaut, Image Proc Ctr, Beijing 100191, Peoples R China

[2] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China

关键词: Blind source separation (BSS);    Independent component analysis (ICA);    Linear predictability;    Nonlinear predictability;   

DOI  :  10.1016/j.cam.2012.03.022

来源: Elsevier

【 摘 要 】

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. (C) 2012 Elsevier B.V. All rights reserved.

【 授权许可】

Free