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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2013: Intelligent Data Engineering and Automated Learning – IDEAL 2013 pp 234–241Cite as

SVD Based Graph Regularized Matrix Factorization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

Graph regularized matrix factorization has been proposed by Cai et al. (2011) with an alternate optimization strategy due to the difficulty of close form solution. In this paper, we develop a novel method for graph regularized matrix factorization. This method is based on Singular Value Decomposition (SVD), and its solution is close formed. We carried out experiments on a database for the task of clustering problem to shown the advantage of the proposed method.

This work was supported by the State Key Laboratory for Novel Software Technology (Grant No. KFKT2012B17).

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of the Philippines Baguio, Baguio City, 2600, Philippines

    Ephrime A. Vidar & Sweedy K. Alvindia

  2. State Key Laboratory for Novel Software Technology, Nanjing University, P.R. China

    Sweedy K. Alvindia

Authors
  1. Ephrime A. Vidar
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  2. Sweedy K. Alvindia
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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Vidar, E.A., Alvindia, S.K. (2013). SVD Based Graph Regularized Matrix Factorization. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_29

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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