A Convex Cauchy-Schwarz DivergenceMeasure for Blind Source Separation
Abstract
We propose a new class of divergence measures for Independent Component Analysis (ICA) for the demixing of multiple source mixtures. We call it the Convex Cauchy-Schwarz Divergence (CCS-DIV), and it is formed by integrating convex functions into the Cauchy-Schwarz inequality. The new measure is symmetric and the degree of its curvature with respect to the joint-distribution can be tuned by a (convexity) parameter. The CCS-DIV is able to speed-up the search process in the parameter space and produces improved demixing performance. An algorithm, generated from the proposed divergence, is developed which is employing the non-parametric Parzen window-based distribution. Simulation evidence is presented to verify and quantify its superior performance in comparison to state-of-the art approaches.
Cite
@article{arxiv.1604.04666,
title = {A Convex Cauchy-Schwarz DivergenceMeasure for Blind Source Separation},
author = {Zaid Albataineh and Fathi M. Salem},
journal= {arXiv preprint arXiv:1604.04666},
year = {2016}
}
Comments
11 pages, 10 figures. arXiv admin note: substantial text overlap with arXiv:1408.0192