Classification at incomplete training information: Usage of group clustering to improve performance

Vladimir Berikov, Yedilkhan Amirgaliyev, Lyailya Cherikbayeva, Didar Yedilkhan, Bakyt Tulegenova

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a method for semi-supervised classification based on a group solution to cluster analysis in combination with Laplacian regularization of similarity graph. The averaged co-association matrix obtained with the cluster ensemble is considered as a similarity matrix in the regularization context. We use a low-rank representation of the matrix that allows us to speed-up computations and save memory in the solution of the derived system of linear equations. Both theoretical studies and numerical experiments on artificial data and hyperspectral imagery confirm the efficiency of the method.

Original languageEnglish
Pages (from-to)5048-5060
Number of pages13
JournalJournal of Theoretical and Applied Information Technology
Volume97
Issue number19
Publication statusPublished - 1 Jan 2019

Keywords

  • Cluster Ensemble
  • Co-Association Matrix
  • Low-Rank Representation
  • Semi-Supervised Learning

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