Blind separation of sparse sources with relative Newton method

Michael Zibulevsky

doi:10.1117/12.505053

13 November 2003 Blind separation of sparse sources with relative Newton method

Michael Zibulevsky

Proceedings Volume 5207, Wavelets: Applications in Signal and Image Processing X; (2003) https://doi.org/10.1117/12.505053
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

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.

Citation Download Citation

Michael Zibulevsky "Blind separation of sparse sources with relative Newton method", Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); https://doi.org/10.1117/12.505053

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