Hybrid linear and nonlinear complexity pursuit for blind source separation

作者：Shi, Zhenwei^*; Zhang, Hongjuan; Jiang, Zhiguo

来源：Journal of Computational and Applied Mathematics, 2012, 236(14): 3434-3444.

DOI：10.1016/j.cam.2012.03.022

• Blind source separation (BSS)
• Independent component analysis (ICA)
• Linear predictability
• Nonlinear predictability

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.

• 出版日期2012-8
• 单位北京航空航天大学; 上海大学