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Semi-supervised Classification Using Multiple Clustering and Low-Rank Matrix Operations

  • Vladimir BerikovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11548)

Abstract

This paper proposes a semi-supervised classification method which combines machine learning regularization framework and cluster ensemble approach. We use the low-rank decomposition of the co-association matrix of the ensemble to significantly speed up calculations and save memory. Numerical experiments using Monte Carlo approach demonstrate the efficiency of the proposed method.

Keywords

Semi-supervised classification Cluster ensemble Co-association matrix Regularization Low-rank matrix decomposition 

Notes

Acknowledgments

The work was supported by the program of Fundamental Scientific Researches of the RAS, project 0314-2019-0015 of the Sobolev Institute of mathematics. The research was partly supported by RFBR grants 18-07-00600, 18-29-09041mk and partly by the Russian Ministry of Science and Higher Education under Project 5-100. The author thanks anonymous reviewers for helpful comments.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sobolev Institute of MathematicsNovosibirskRussia
  2. 2.Novosibirsk State UniversityNovosibirskRussia

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