Maximum contrast analysis for nonnegative blind source separation

Yang, Zuyuan, Xiang, Yong and Xie, Shengli 2011, Maximum contrast analysis for nonnegative blind source separation, Computers and mathematics with applications, vol. 62, no. 11, pp. 3997-4006.

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Title Maximum contrast analysis for nonnegative blind source separation
Author(s) Yang, Zuyuan
Xiang, Yong
Xie, Shengli
Journal name Computers and mathematics with applications
Volume number 62
Issue number 11
Start page 3997
End page 4006
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2011-12
ISSN 0898-1221
1873-7668
Keyword(s) iterative determinant maximization
maximum contrast analysis
nonnegative blind source separation
Summary In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.
Language eng
Field of Research 090609 Signal Processing
Socio Economic Objective 890104 Mobile Telephone Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2011, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044087

Document type: Journal Article
Collection: School of Information Technology
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