Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures (CROSBI ID 663149)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Kopriva, Ivica
engleski
Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures
An approach is proposed for underdetermined blind separation of nonnegative dependent (overlapped) sources from their nonlinear mixtures. The method performs empirical kernel maps based mappings of original data matrix onto reproducible kernel Hilbert spaces (RKHSs). Provided that sources comply with probabilistic model that is sparse in support and amplitude nonlinear underdetermined mixture model in the input space becomes overdetermined linear mixture model in RKHS comprised of original sources and their mostly second-order monomials. It is assumed that linear mixture models in different RKHSs share the same representation, i.e. the matrix of sources. Thus, we propose novel sparseness regularized joint nonnegative matrix factorization method to separate sources shared across different RKHSs. The method is validated comparatively on numerical problem related to extraction of eight overlapped sources from three nonlinear mixtures.
underdetermined blind source separation ; nonlinear mixtures ; empirical kernel map ; joint nonnegative matrix factorization ; sparseness
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Podaci o prilogu
107-115.
2018.
objavljeno
10.1007/978-3-319-93764-9_11
Podaci o matičnoj publikaciji
Lecture Notes in Computer Science 10891
Deville, Y ; Gannot, S ; Mason, D ; Plumbley, M. D ; Ward, D
Chenai: Springer
978-3-319-93763-2
Podaci o skupu
14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2018)
poster
02.07.2018-06.07.2018
Guildford, Ujedinjeno Kraljevstvo