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.
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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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Berikov, V. (2019). Semi-supervised Classification Using Multiple Clustering and Low-Rank Matrix Operations. In: Khachay, M., Kochetov, Y., Pardalos, P. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2019. Lecture Notes in Computer Science(), vol 11548. Springer, Cham. https://doi.org/10.1007/978-3-030-22629-9_37
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DOI: https://doi.org/10.1007/978-3-030-22629-9_37
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