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Proceedings Paper

Blind separation of sparse sources with relative Newton method
Author(s): Michael Zibulevsky
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Paper Abstract

We study a relative optimization framework for the quasi-maximum likelihood blind source separation and relative Newton method as its particular instance. Convergence of the Newton method is stabilized by the line search and by the modification of the Hessian, which forces its positive definiteness. The structure of the Hessian allows fast approximate inversion. In order to separate sparse sources, we use a non-linearity based on smooth approximation to the absolute value function. Sequential optimization with the gradual reduction of the smoothing parameter leads to the super-efficient separation.

Paper Details

Date Published: 13 November 2003
PDF: 9 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.505053
Show Author Affiliations
Michael Zibulevsky, Technion-Israel Institute of Technology (Israel)


Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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