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Semi-Paired Multiview Clustering Based on Nonnegative Matrix Factorization

  • COMPUTER METHODS
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Journal of Computer and Systems Sciences International Aims and scope

Abstract

As data that have multiple views become widely available, the clusterization of such data based on nonnegative matrix factorization has been attracting greater attention. In the majority of studies, the statement in which all objects have images in all representations is considered. However, this is often not the case in practical problems. To resolve this issue, a novel semi-paired multiview clustering algorithm is proposed. For incomplete data, it is assumed that their views have the same indicator vector, and the paired matrix is introduced. The objects that are close to each other in each view must have identical indicators, which makes regularization and reconstruction of the manifold geometric structure possible. The proposed algorithm can work both with incomplete and complete data having multiple views. The experimental results obtained on four datasets prove its effectiveness compared to other modern algorithms.

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Funding

This paper was partially supported by the Chinese NUAA funding, project no. NG2019004; NSFC funding, project no. 61703206; and by the Russian Foundation for Basic Research, project no. 19-01-00625.

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control”, Russian Academy of Sciences, 119333, Moscow, Russia

    I. A. Matveev

  2. Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China

    X. Yao & X. Chen

  3. School of Computer Science and Engineering, Southeast University, 210096, Nanjing, China

    H. Xue

  4. Institute of Communications Engineering, Army Engineering University of PLA, 210007, Nanjing, China

    L. Yu

Authors
  1. X. Yao
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  2. X. Chen
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  3. I. A. Matveev
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  4. H. Xue
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  5. L. Yu
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Corresponding authors

Correspondence to X. Chen or I. A. Matveev.

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Translated by A. Klimontovich

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Yao, X., Chen, X., Matveev, I.A. et al. Semi-Paired Multiview Clustering Based on Nonnegative Matrix Factorization. J. Comput. Syst. Sci. Int. 58, 579–594 (2019). https://doi.org/10.1134/S1064230719040117

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  • DOI: https://doi.org/10.1134/S1064230719040117

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