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Global Minimization of the Projective Nonnegative Matrix Factorization

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5507))

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Abstract

The Nonnegative Matrix Factorization (NMF) is a widely used method in approximating high dimensional data. All the NMF type methods find only the local minimizers. In this paper, we use filled function method to find the global minimizer of the Projective Nonnegative Matrix Factorization optimal problem.

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References

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Author information

Authors and Affiliations

  1. Neural Networks Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Zhijian Yuan

Authors
  1. Zhijian Yuan
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Editor information

Editors and Affiliations

  1. Network Design and Research Center, Kyushu Institute of Technology,, 680-4, Kawazu, Iizuka,, 820-8502, Fukuoka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street,, 1142, Auckland, New Zealand

    George Coghill

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Yuan, Z. (2009). Global Minimization of the Projective Nonnegative Matrix Factorization. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_120

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  • DOI: https://doi.org/10.1007/978-3-642-03040-6_120

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03039-0

  • Online ISBN: 978-3-642-03040-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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