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GPU-Accelerated Non-negative Matrix Factorization for Text Mining

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Natural Language Processing and Information Systems (NLDB 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7337))

Abstract

An implementation of the non-negative matrix factorization algorithm for the purpose of text mining on graphics processing units is presented. Performance gains of more than one order of magnitude are obtained.

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Author information

Authors and Affiliations

  1. Faculty of Cybernetics, Taras Shevchenko National University of Kyiv, Ukraine

    Volodymyr Kysenko, Oleksandr Marchenko & Anatoly Anisimov

  2. Institute for Microelectronics, TU Wien, Austria

    Karl Rupp & Siegfried Selberherr

  3. Institute for Analysis and Scientific Computing, TU Wien, Austria

    Karl Rupp

Authors
  1. Volodymyr Kysenko
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  2. Karl Rupp
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  3. Oleksandr Marchenko
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  4. Siegfried Selberherr
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  5. Anatoly Anisimov
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Editor information

Editors and Affiliations

  1. Information Science Department, University of Groningen, Oude Kijk in ’t Jatstraat 26, 9712 EK, Groningen, The Netherlands

    Gosse Bouma

  2. Faculty of Economics and Business, University of Groningen, Nettelbosje 2, 9747 AE, Groningen, The Netherlands

    Ashwin Ittoo  & Hans Wortmann  & 

  3. CNAM-Laboratoire Cédric, 292 rue St. Martin, 75141, Paris Cedex 03, France

    Elisabeth Métais

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© 2012 Springer-Verlag Berlin Heidelberg

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Kysenko, V., Rupp, K., Marchenko, O., Selberherr, S., Anisimov, A. (2012). GPU-Accelerated Non-negative Matrix Factorization for Text Mining. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_15

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  • DOI: https://doi.org/10.1007/978-3-642-31178-9_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31177-2

  • Online ISBN: 978-3-642-31178-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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