A METHOD BASED ON L-BFGS TO SOLVE CONSTRAINED COMPLEX-VALUED ICA

被引：0

作者：

Nguyen, Anh H. T. ^{[1
]}

Reju, V. G. ^{[2
]}

Khong, Andy W. H. ^{[1
]}

机构：

[1] Nanyang Technol Univ, Singapore, Singapore

[2] Cochin Univ Sci & Technol, Kochi, Kerala, India

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2019年

基金：

新加坡国家研究基金会;

关键词：

complex-valued ICA; L-BFGS; blind source separation; INDEPENDENT COMPONENT ANALYSIS; ALGORITHM; OPTIMIZATION; DIVERSITY;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Complex-valued independent component analysis (ICA) is a celebrated method in blind separation of complex-valued signals. In this paper, we propose to transform the constrained optimization problems of complex-valued ICA into unconstrained optimization problems which can be solved by limited-memory Broyden-Fletcher-Goldfarb-Shanno update (L-BFGS). As opposed to previous approaches, the proposed method does not apply any restriction on the Hessian matrix of ICA cost function. It can separate mixed sub Gaussian, super-Gaussian, circular, and non-circular sources. Simulations show promising results.

引用

页码：4370 / 4374

页数：5

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