Flexible independent component analysis

被引：38

作者：

Choi, S ^{[1
]}

Cichocki, A ^{[1
]}

Amari, S ^{[1
]}

机构：

[1] Chungbuk Natl Univ, Sch Elect & Elect Engn, Chungbuk 361763, South Korea

来源：

NEURAL NETWORKS FOR SIGNAL PROCESSING VIII | 1998年

关键词：

D O I：

10.1109/NNSP.1998.710637

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a flexible independent component analysis (ICA) algorithm which can separate mixtures of sub- and super-Gaussian source signals with self-adaptive nonlinearities. The flexible ICA algorithm in the framework of natural Riemannian gradient, is derived using the parameterized generalized Gaussian density model. The nonlinear function in the flexible ICA algorithm is self-adaptive and is controlled by Gaussian exponent. Computer simulation results confirm the validity and high performance of the proposed algorithm.

引用

页码：83 / 92

页数：10