Maximum contrast analysis for nonnegative blind source separation

Authors:
Zuyuan Yang;Yong Xiang;Shengli Xie
Affiliations:
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China;School of Information Technology, Deakin University, Burwood Campus, Melbourne, VIC 3125, Australia;Faculty of Automation, Guangdong University of Technology, Guangzhou, 510006, China
Venue:
Computers & Mathematics with Applications
Year:
2011

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Abstract

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.