Semi-supervised Classification Using Multiple Clustering and Low-Rank Matrix Operations
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.
KeywordsSemi-supervised classification Cluster ensemble Co-association matrix Regularization Low-rank matrix decomposition
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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