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Non-negative Matrix Factorization on GPU

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Networked Digital Technologies (NDT 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 87))

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Abstract

Today, the need of large data collection processing increase. Such type of data can has very large dimension and hidden relationships. Analyzing this type of data leads to many errors and noise, therefore, dimension reduction techniques are applied. Many techniques of reduction were developed, e.g. SVD, SDD, PCA, ICA and NMF. Non-negative matrix factorization (NMF) has main advantage in processing of non-negative values which are easily interpretable as images, but other applications can be found in different areas as well. Both, data analysis and dimension reduction methods, need a lot of computation power. In these days, many algorithms are rewritten with the GPU utilization, because GPU brings massive parallel architecture and very good ratio between performance and price. This paper introduce computation of NMF on GPU using CUDA technology.

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Author information

Authors and Affiliations

  1. Department of Computer Science, FEI, VSB - Technical University of Ostrava, 17. listopadu 15, 708 33, Ostrava-Poruba, Czech Republic

    Jan Platoš, Petr Gajdoš, Pavel Krömer & Václav Snášel

Authors
  1. Jan Platoš
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  2. Petr Gajdoš
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  3. Pavel Krömer
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  4. Václav Snášel
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Editor information

Editors and Affiliations

  1. Department of Software Engineering, Charles University in Prague, Malostranske namesti 25, 118 00, Prague, Czech Republic

    Filip Zavoral

  2. Charles University, Prague, Czech Republic

    Jakub Yaghob

  3. Al Imam University, Riyadh, Saudi Arabia

    Pit Pichappan

  4. Jordan University of Science and Technology, Irbid, Jordan

    Eyas El-Qawasmeh

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Platoš, J., Gajdoš, P., Krömer, P., Snášel, V. (2010). Non-negative Matrix Factorization on GPU. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14292-5_4

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  • DOI: https://doi.org/10.1007/978-3-642-14292-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14291-8

  • Online ISBN: 978-3-642-14292-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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