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Improving NNMFPACK with heterogeneous and efficient kernels for ß-divergence metrics

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Improving NNMFPACK with heterogeneous and efficient kernels for ß-divergence metrics

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Díaz-Gracia, N.; Cocaña-Fernández, A.; Alonso-González, M.; Martínez Zaldívar, FJ.; Cortina, R.; García Mollá, VM.; Alonso, P.... (2015). Improving NNMFPACK with heterogeneous and efficient kernels for ß-divergence metrics. Journal of Supercomputing. 71(5):1846-1856. https://doi.org/10.1007/s11227-014-1363-y

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Título: Improving NNMFPACK with heterogeneous and efficient kernels for ß-divergence metrics
Autor: Díaz-Gracia, N. Cocaña-Fernández, A. Alonso-González, M: Martínez Zaldívar, Francisco José Cortina, Raquel García Mollá, Víctor Manuel Alonso, P. Ranilla, J. Vidal Maciá, Antonio Manuel
Entidad UPV: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
NnmfPack is a library for the nonnegative matrix factorization (NNMF) problem. Nowadays NNMF is an essential tool in many fields spanning machine learning, data analysis, image analysis or audio source separation, among ...[+]
Palabras clave: NNMF , Parallel library , GPU , Intel MIC , Multi-core , Many-core
Derechos de uso: Cerrado
Fuente:
Journal of Supercomputing. (issn: 1573-0484 )
DOI: 10.1007/s11227-014-1363-y
Editorial:
Springer Verlag (Germany)
Versión del editor: http://dx.doi.org/10.1007/s11227-014-1363-y
Código del Proyecto:
info:eu-repo/grantAgreement/MINECO//TEC2012-38142-C04-01/ES/PROCESADO DISTRIBUIDO Y COLABORATIVO DE SEÑALES SONORAS: CONTROL ACTIVO/
info:eu-repo/grantAgreement/MINECO//TEC2012-38142-C04-04/ES/PROCESADO DISTRIBUIDO Y COLABORATIVO DE SEÑALES SONORAS: COMPUTACION DISTRIBUIDA/
info:eu-repo/grantAgreement/GVA//ISIC%2F2012%2F006/
info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2014%2F003/ES/Computación y comunicaciones de altas prestaciones y aplicaciones en ingeniería/
Agradecimientos:
This work has been partially supported by "Ministerio de Economia y Competitividad" from Spain, under the projects TEC2012-38142-C04-01 and TEC2012-38142-C04-04 and by ISIC/2012/006 and PROMETEO FASE II 2014/003 projects ...[+]
Tipo: Artículo

References

Battenberg E, Freed A, Wessel D (2010) Advances in the parallelization of music and audio applications. In: Proceedings of the International Computer Music Conference, New York

Wnag J, Zhong W, Zhang J (2006) NNMF-based factorization techniques for high-accuracy privacy protection on non-negative-valued datasets. In: Proceedings of the Sixth IEEE International Conference on Computing and Processing, Data Mining Workshops ICDM Workshops, pp 513–517

Rodriguez-Serrano FJ, Carabias-Orti JJ, Vera-Candeas P, Virtanen T, Ruiz-Reyes N (2012) Multiple instrument mixtures source separation evaluation using instrument-dependent NMF models. In: Proceedings of the 10th international conference on latent variable analysis and signal separation, March 12–15, Tel Aviv, Israel. LNCS, vol 7191. Springer, Berlin, pp 380–387 [+]
Battenberg E, Freed A, Wessel D (2010) Advances in the parallelization of music and audio applications. In: Proceedings of the International Computer Music Conference, New York

Wnag J, Zhong W, Zhang J (2006) NNMF-based factorization techniques for high-accuracy privacy protection on non-negative-valued datasets. In: Proceedings of the Sixth IEEE International Conference on Computing and Processing, Data Mining Workshops ICDM Workshops, pp 513–517

Rodriguez-Serrano FJ, Carabias-Orti JJ, Vera-Candeas P, Virtanen T, Ruiz-Reyes N (2012) Multiple instrument mixtures source separation evaluation using instrument-dependent NMF models. In: Proceedings of the 10th international conference on latent variable analysis and signal separation, March 12–15, Tel Aviv, Israel. LNCS, vol 7191. Springer, Berlin, pp 380–387

Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, July 28–Aug 1, Toronto, Canada, pp 267–273

Berry MW, Browne M, Langville A, Pauca V, Plemmons R (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 52:155–173

Devajaran K (2008) Nonnegative matrix factorization: an analytical and interpretative tool in computational biology. PLoS Comput Biol 4(7):e1000029. doi: 10.1371/journal.pcbi.1000029

Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization., Advances in neural information processing systemsMIT Press, Cambridge

Kim J, Park H (2008) Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J Matrix Anal Appl 30:713–730

Guan N, Tao D, Luo Z, Yuan B (2012) NeNMF: An optimal gradient method for non-negative matrix factorization. IEEE Trans Signal Process 60(6):2882–2898

Cichocki A, Phan AH (2009) Fast local algorithms for large scale nonnegative matrix and tensor factorizations. In: Proceedings of IEICE transactions on fundamentals of electronics communications and computer sciences, E92-A, pp 708–721

Cichocki A, Zdunek R, Amari SI (2007) Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization. In: Proceedings of the 7th international conference on independent component analysis and signal separation, September 9–12, London, UK. LNCS, vol 4666. Springer, Berlin, pp 169–176

Alonso P, García VM, Martínez-Zaldívar FJ, Salazar A, Vergara L, Vidal AM (2014) Parallel approach to NNMF on multicore architecture. J Supercomput. 70(2):564–576

Díaz-Gracia N, Cocaña-Fernández A, Alonso-González M, Martínez-Zaldívar FJ, Cortina R, García-Mollá VM, Alonso P, Ranilla J, Vidal AM (2014) NNMFPACK: a versatile approach to an NNMF parallel library. In: Proceedings of the 2014 international conference on computational and mathematical methods in science and engineering, Cádiz, 2014, pp 456–465

Carabias-Orti JJ, Rodriguez-Serrano FJ, Vera-Candeas P, Cañadas-Quesada FJ, Ruiz-Reyes N (2013) Constrained non-negative sparse coding using learnt instrument templates for real time music transcription. Eng Appl AI 26(7):1671–1680

Carabias-Orti JJ, Virtanen T, Vera-Candeas P, Ruiz-Reyes N, Cañadas-Quesada FJ (2011) Musical instrument sound multi-excitation model for non-negative spectrogram factorization. IEEE J Select Topics Signal Process 5(6):1144–1158

Minami M, Eguchi S (2002) Robust blind source separation by beta-divergence. Neural Comput 14:1859–1886

Févotte C, Bertin N, Durrieu J-L (2009) Nonnegative matrix factorization with the Itakura-Saito divergence: with application to music analysis. Neural Comput 21:793–830

Golub GH, Van Loan CF (1996) Matrix Comput. Johns Hopkins University Press, Baltimore

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