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dc.contributor.author | García Mollá, Víctor Manuel | es_ES |
dc.contributor.author | San Juan-Sebastian, Pablo | es_ES |
dc.contributor.author | Virtanen, T. | es_ES |
dc.contributor.author | Vidal Maciá, Antonio Manuel | es_ES |
dc.contributor.author | Alonso-Jordá, Pedro | es_ES |
dc.date.accessioned | 2019-07-11T20:01:51Z | |
dc.date.available | 2019-07-11T20:01:51Z | |
dc.date.issued | 2019 | es_ES |
dc.identifier.issn | 1051-2004 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/123503 | |
dc.description.abstract | [EN] In this paper, we propose, describe, and test a modification of the K-SVD algorithm. Given a set of training data, the proposed algorithm computes an overcomplete dictionary by minimizing the ß-divergence () between the data and its representation as linear combinations of atoms of the dictionary, under strict sparsity restrictions. For the special case , the proposed algorithm minimizes the Frobenius norm and, therefore, for the proposed algorithm is equivalent to the original K-SVD algorithm. We describe the modifications needed and discuss the possible shortcomings of the new algorithm. The algorithm is tested with random matrices and with an example based on speech separation. | es_ES |
dc.description.sponsorship | This work has been partially supported by the EU together with the Spanish Government through TEC2015-67387-C4-1-R (MINECO/FEDER) and by Programa de FPU del Ministerio de Educacion, Cultura y Deporte FPU13/03828 (Spain). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Digital Signal Processing | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | K-SVD | es_ES |
dc.subject | Nonnegative K-SVD | es_ES |
dc.subject | Beta-divergence | es_ES |
dc.subject | NMF | es_ES |
dc.subject | Matching pursuit algorithms | es_ES |
dc.subject.classification | CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL | es_ES |
dc.title | Generalization of the K-SVD algorithm for minimization of ß-divergence | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.dsp.2019.05.001 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TEC2015-67387-C4-1-R/ES/SMART SOUND PROCESSING FOR THE DIGITAL LIVING/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD//FPU13%2F03828/ES/FPU13%2F03828/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | García Mollá, VM.; San Juan-Sebastian, P.; Virtanen, T.; Vidal Maciá, AM.; Alonso-Jordá, P. (2019). Generalization of the K-SVD algorithm for minimization of ß-divergence. Digital Signal Processing. 92:47-53. https://doi.org/10.1016/j.dsp.2019.05.001 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.dsp.2019.05.001 | es_ES |
dc.description.upvformatpinicio | 47 | es_ES |
dc.description.upvformatpfin | 53 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 92 | es_ES |
dc.relation.pasarela | S\387200 | es_ES |
dc.contributor.funder | Ministerio de Economía, Industria y Competitividad | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministerio de Educación, Cultura y Deporte | es_ES |