@inproceedings{555b93501cb948179a653b2f8b60a59a,
title = "Semi-supervised classification using multiple clustering and low-rank matrix operations",
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 = "Cluster ensemble, Co-association matrix, Low-rank matrix decomposition, Regularization, Semi-supervised classification",
author = "Vladimir Berikov",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-22629-9_37",
language = "English",
isbn = "9783030226282",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "529--540",
editor = "Michael Khachay and Panos Pardalos and Yury Kochetov",
booktitle = "Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings",
address = "Germany",
note = "18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019 ; Conference date: 08-07-2019 Through 12-07-2019",
}