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Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection

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  • Yong-Jing Hao
  • Ying-Lian Gao
  • Mi-Xiao Hou
  • Ling-Yun Dai
  • Jin-Xing Liu

Abstract

Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the above problem, this paper proposed a new method called Hypergraph Regularized Discriminative Nonnegative Matrix Factorization (HDNMF), which captures intrinsic geometry by constructing hypergraphs rather than simple graphs. The introduction of the hypergraph method allows high-order relationships between samples to be considered, and the introduction of label information enables the method to have discriminative effect. Both the hypergraph Laplace and the discriminative label information are utilized together to learn the projection matrix in the standard method. In addition, we offered a corresponding multiplication update solution for the optimization. Experiments indicate that the method proposed is more effective by comparing with the earlier methods.

Suggested Citation

  • Yong-Jing Hao & Ying-Lian Gao & Mi-Xiao Hou & Ling-Yun Dai & Jin-Xing Liu, 2019. "Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection," Complexity, Hindawi, vol. 2019, pages 1-12, August.
    • Handle: RePEc:hin:complx:7081674
    DOI: 10.1155/2019/7081674
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Brito, M. R. & Chávez, E. L. & Quiroz, A. J. & Yukich, J. E., 1997. "Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection," Statistics & Probability Letters, Elsevier, vol. 35(1), pages 33-42, August.
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