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Pregled bibliografske jedinice broj: 943262

Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures

Kopriva, Ivica
Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures // Lecture Notes in Computer Science 10891 / Deville, Y ; Gannot, S ; Mason, D ; Plumbley, M. D ; Ward, D (ur.).
Chenai: Springer, 2018. str. 107-115 doi:10.1007/978-3-319-93764-9_11 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures

Autori
Kopriva, Ivica

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Lecture Notes in Computer Science 10891 / Deville, Y ; Gannot, S ; Mason, D ; Plumbley, M. D ; Ward, D - Chenai : Springer, 2018, 107-115

ISBN
978-3-319-93763-2

Skup
14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2018)

Mjesto i datum
Guildford, Ujedinjeno Kraljevstvo, 02.07.2018. - 06.07.2018

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
underdetermined blind source separation ; nonlinear mixtures ; empirical kernel map ; joint nonnegative matrix factorization ; sparseness

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo



POVEZANOST RADA

Projekti:
IP-2016-06-5235 - Strukturne dekompozicije empirijskih podataka za računalno potpomognutu dijagnostiku bolesti (DEDAD) (Kopriva, Ivica, HRZZ - 2016-06) ( CroRIS)

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Ivica Kopriva (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi doi.org

Citiraj ovu publikaciju:

Kopriva, Ivica
Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures // Lecture Notes in Computer Science 10891 / Deville, Y ; Gannot, S ; Mason, D ; Plumbley, M. D ; Ward, D (ur.).
Chenai: Springer, 2018. str. 107-115 doi:10.1007/978-3-319-93764-9_11 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Kopriva, I. (2018) Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures. U: Deville, Y., Gannot, S., Mason, D., Plumbley, M. & Ward, D. (ur.)Lecture Notes in Computer Science 10891 doi:10.1007/978-3-319-93764-9_11.
@article{article, author = {Kopriva, Ivica}, year = {2018}, pages = {107-115}, DOI = {10.1007/978-3-319-93764-9\_11}, keywords = {underdetermined blind source separation, nonlinear mixtures, empirical kernel map, joint nonnegative matrix factorization, sparseness}, doi = {10.1007/978-3-319-93764-9\_11}, isbn = {978-3-319-93763-2}, title = {Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures}, keyword = {underdetermined blind source separation, nonlinear mixtures, empirical kernel map, joint nonnegative matrix factorization, sparseness}, publisher = {Springer}, publisherplace = {Guildford, Ujedinjeno Kraljevstvo} }
@article{article, author = {Kopriva, Ivica}, year = {2018}, pages = {107-115}, DOI = {10.1007/978-3-319-93764-9\_11}, keywords = {underdetermined blind source separation, nonlinear mixtures, empirical kernel map, joint nonnegative matrix factorization, sparseness}, doi = {10.1007/978-3-319-93764-9\_11}, isbn = {978-3-319-93763-2}, title = {Joint Nonnegative Matrix Factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures}, keyword = {underdetermined blind source separation, nonlinear mixtures, empirical kernel map, joint nonnegative matrix factorization, sparseness}, publisher = {Springer}, publisherplace = {Guildford, Ujedinjeno Kraljevstvo} }

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