Semi-supervised classification using multiple clustering and low-rank matrix operations

Semi-supervised classification using multiple clustering and low-rank matrix operations Full article

Journal

Lecture Notes in Computer Science
ISSN: 0302-9743 , E-ISSN: 1611-3349

Output data

Year: 2019, Volume: 11548 LNCS, Pages: 529-540 Pages count : 12 DOI: 10.1007/978-3-030-22629-9_37

Tags

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

Authors

Berikov V. ^1,2

Affiliations

1	Sobolev Institute of Mathematics, Koptyug pr. 4, Novosibirsk, 630090, Russian Federation
2	Novosibirsk State University, Universitetsky pr. 1, Novosibirsk, 630090, Russian Federation

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. © Springer Nature Switzerland AG 2019.

Berikov V.
Semi-supervised classification using multiple clustering and low-rank matrix operations
Lecture Notes in Computer Science. 2019. V.11548 LNCS. P.529-540. DOI: 10.1007/978-3-030-22629-9_37 Scopus OpenAlex

Scopus:	2-s2.0-85067677189
OpenAlex:	W2950355158

DB	Citing
Scopus	7
OpenAlex	6