Standard

Semi-supervised classification using multiple clustering and low-rank matrix operations. / Berikov, Vladimir.

Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. ed. / Michael Khachay; Panos Pardalos; Yury Kochetov. Springer-Verlag GmbH and Co. KG, 2019. p. 529-540 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11548 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Berikov, V 2019, Semi-supervised classification using multiple clustering and low-rank matrix operations. in M Khachay, P Pardalos & Y Kochetov (eds), Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11548 LNCS, Springer-Verlag GmbH and Co. KG, pp. 529-540, 18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019, Ekaterinburg, Russian Federation, 08.07.2019. https://doi.org/10.1007/978-3-030-22629-9_37

APA

Berikov, V. (2019). Semi-supervised classification using multiple clustering and low-rank matrix operations. In M. Khachay, P. Pardalos, & Y. Kochetov (Eds.), Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings (pp. 529-540). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11548 LNCS). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-030-22629-9_37

Vancouver

Berikov V. Semi-supervised classification using multiple clustering and low-rank matrix operations. In Khachay M, Pardalos P, Kochetov Y, editors, Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. Springer-Verlag GmbH and Co. KG. 2019. p. 529-540. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-22629-9_37

Author

Berikov, Vladimir. / Semi-supervised classification using multiple clustering and low-rank matrix operations. Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings. editor / Michael Khachay ; Panos Pardalos ; Yury Kochetov. Springer-Verlag GmbH and Co. KG, 2019. pp. 529-540 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@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",

}

RIS

TY - GEN

T1 - Semi-supervised classification using multiple clustering and low-rank matrix operations

AU - Berikov, Vladimir

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Cluster ensemble

KW - Co-association matrix

KW - Low-rank matrix decomposition

KW - Regularization

KW - Semi-supervised classification

UR - http://www.scopus.com/inward/record.url?scp=85067677189&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-22629-9_37

DO - 10.1007/978-3-030-22629-9_37

M3 - Conference contribution

AN - SCOPUS:85067677189

SN - 9783030226282

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 529

EP - 540

BT - Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings

A2 - Khachay, Michael

A2 - Pardalos, Panos

A2 - Kochetov, Yury

PB - Springer-Verlag GmbH and Co. KG

T2 - 18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019

Y2 - 8 July 2019 through 12 July 2019

ER -

ID: 20643467