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Improving NNMFPACK with heterogeneous and efficient kernels for \(\beta \)-divergence metrics

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Abstract

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 others. NnmfPack is an efficient numerical library conceived for shared memory heterogeneous parallel systems, and it supports, from its conception, both conventional multi-core processors and many-core coprocessors. In this article, NnmfPack is extended to handle different metrics options (\(\beta \)-divergence), and some other parallel algorithms have been added and tested. The performance of the new functionalities of NnmfPack is tested, and some precision results of the implementations are showed using an example borrowed from the image processing field.

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Acknowledgments

This work has been partially supported by “Ministerio de Economía 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 of Generalitat Valenciana.

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Authors and Affiliations

  1. Departamento de Informática, Universidad de Oviedo, Gijón, Spain

    N. Díaz-Gracia, A. Cocaña-Fernández, M. Alonso-González, R. Cortina & J. Ranilla

  2. Departamento de Comunicaciones, Universitat Politècnica de València, Valencia, Spain

    F. J. Martínez-Zaldívar

  3. Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain

    V. M. García-Mollá & A. M. Vidal

  4. Departamento de Matemáticas, Universidad de Oviedo, Oviedo, Spain

    P. Alonso

Authors
  1. N. Díaz-Gracia
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  2. A. Cocaña-Fernández
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  3. M. Alonso-González
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  4. F. J. Martínez-Zaldívar
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  5. R. Cortina
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  6. V. M. García-Mollá
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  7. P. Alonso
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  8. J. Ranilla
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  9. A. M. Vidal
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Corresponding author

Correspondence to J. Ranilla.

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Díaz-Gracia, N., Cocaña-Fernández, A., Alonso-González, M. et al. Improving NNMFPACK with heterogeneous and efficient kernels for \(\beta \)-divergence metrics. J Supercomput 71, 1846–1856 (2015). https://doi.org/10.1007/s11227-014-1363-y

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  • DOI: https://doi.org/10.1007/s11227-014-1363-y

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