A Spectral Clustering Approach to Underdetermined Postnonlinear Blind Source Separation of Sparse Sources  

Oleh:

Vaerenbergh, S. Van ; Santamaria, I.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 810-813.
Topik: sources; spectral clustering; approach; postnonlinear blind; source separation; sparse sources

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This letter proposes a clustering - based approach for solving the underdetermined (i. e., fewer mixtures than sources) post non linear blind source separation (PNL BSS) problem when the sources are sparse. Although various algorithms exist for the underdetermined BSS problem for sparse sources, as well as for the PNL BSS problem with as many mixtures as sources, the nonlinear problem in an underdetermined scenario has not been satisfactorily solved yet. The method proposed in this letter aims at inverting the different non linearities, thus reducing the problem to linear underdetermined BSS. To this end, first a spectral clustering technique is applied that clusters the mixture samples into different sets corresponding to the different sources. Then, the inverse non linearities are estimated using a set of multilayer perceptrons (MLP s) that are trained by minimizing a specifically designed cost function. Finally, transforming each mixture by its corresponding inverse non linearity results in a linear underdetermined BSS problem, which can be solved using any of the existing methods.

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