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Sparse Representation of Data and Support Vector Machines (in: Proceedings)

Georgiev, P. ; Theis, Fabian J. ; Ralescu, A.


Abstract

We apply a new Blind Source Separation method (BSS), using sparseness, for identification of overdetermined linear mixing models, as we impose sparseness assumptions on the mixing matrix and no assumptions on the sources like independence or sparseness. We describe a suitable application of our method, for identification of kernel matrices in Support Vector Machines, under assumptions of ...

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