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

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4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings
EditorsMichael Khachay, Panos Pardalos, Yury Kochetov
PublisherSpringer-Verlag GmbH and Co. KG
Pages529-540
Number of pages12
ISBN (Print)9783030226282
DOIs
Publication statusPublished - 1 Jan 2019
Event18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019 - Ekaterinburg, Russian Federation
Duration: 8 Jul 201912 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11548 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019
CountryRussian Federation
CityEkaterinburg
Period08.07.201912.07.2019

Keywords

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

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