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Weakly Supervised Regression Using Manifold Regularization and Low-Rank Matrix Representation

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

Theoretical Cybernetics Section

DOI

https://doi.org/10.1007/978-3-030-77876-7_30
Final published version

Vladimir Berikov
Alexander Litvinenko

Original language	English
Title of host publication	Mathematical Optimization Theory and Operations Research - 20th International Conference, MOTOR 2021, Proceedings
Editors	Panos Pardalos, Michael Khachay, Alexander Kazakov
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	447-461
Number of pages	15
ISBN (Print)	9783030778750
DOIs	https://doi.org/10.1007/978-3-030-77876-7_30
Publication status	Published - 2021
Event	20th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2021 - Irkutsk, Russian Federation Duration: 5 Jul 2021 → 10 Jul 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12755 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2021
Country/Territory	Russian Federation
City	Irkutsk
Period	05.07.2021 → 10.07.2021

OECD FOS+WOS

1.02 COMPUTER AND INFORMATION SCIENCES
1.01 MATHEMATICS

Research areas

Cluster ensemble, Co-association matrix, Low-rank matrix decomposition, Manifold regularization, Weakly supervised learning

ID: 34146129