**A Convex Cauchy-Schwarz
Divergence Measure for Blind Source Separation**

**
We propose a new class of divergence measures for
Independent Component Analysis (ICA) for the
de-mixing of sources.
The Convex Cauchy-Schwarz Divergence (CCS-DIV)
is formed by integrating convex functions into the Cauchy-Schwarz inequality.
The new measure is symmetric and convex, where
the degree of convexity can be tuned by a (convexity) parameter.
The
CCS-DIV is more likely to attain optimal minima and speedup the search process
in ICA. A non-parametric algorithm, generated from the proposed divergence, is
developed with convexity parameters and is employing the Parzen window-based
distribution. **

- Zaid Albataineh and Fathi Salem, “
**Convex Cauchy-Schwarz Independent Component Analysis CCS-ICA for Blind Source Separation**,” IEEE Trans. Neural Netw. 2014 - Zaid
Albataineh and Fathi Salem, “
**RobustICA-Based Algorithm for Blind Separation of Convolutive Mixtures**” IEEE Trans. Circuit and System I 2014 - Zaid Albataineh and Fathi Salem, “
**A Convex Cauchy-Schwarz Divergence Measure for Blind Source Separation**” Springer. Neural Processing Letter 2014 - Zaid Albataineh and Fathi Salem, “
**Two Pairwise Iterative Schemes For High Dimensional Blind Source Separation,**” Springer. Neural Processing Letter 2014