A Convex Analysis Framework for Blind Separation of Non-Negative Sources

This paper presents a new framework for blind source separation (BSS) of non-negative source signals. The proposed framework, referred herein to as convex analysis of mixtures of non-negative sources (CAMNS), is deterministic requiring no source independence assumption, the entrenched premise in many existing (usually statistical) BSS frameworks. The development is based on a special assumption called local dominance. It is a good assumption for source signals exhibiting sparsity or high contrast, and thus is considered realistic to many real-world problems such as multichannel biomedical imaging. Under local dominance and several standard assumptions, we apply convex analysis to establish a new BSS criterion, which states that the source signals can be perfectly identified (in a blind fashion) by finding the extreme points of an observation-constructed polyhedral set. Methods for fulfilling the CAMNS criterion are also derived, using either linear programming or simplex geometry. Sim...

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Tsung-Han Chan, Wing-Kin Ma, Chong-Yung Chi, Yue Wang
• Keywords: Non-negative Source Signals, Non-negative Sources, Source Signals, TSP 2008