Speed-up and multi-view extensions to subclass discriminant analysis

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Abstract

In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based on graph embedding and spectral regression approaches that involve eigendecomposition of the corresponding Laplacian matrix and regression to its eigenvectors. We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process. Furthermore, we formulate a novel criterion for multi-view subclass discriminant analysis and show that an efficient solution to it can be obtained in a similar manner to the single-view case. We evaluate the proposed methods on nine single-view and nine multi-view datasets and compare them with related existing approaches. Experimental results show that the proposed solutions achieve competitive performance, often outperforming the existing methods. At the same time, they significantly decrease the training time.

Original languageEnglish
Article number107660
JournalPattern Recognition
Volume111
Early online date19 Sep 2020
DOIs
Publication statusPublished - Mar 2021
Publication typeA1 Journal article-refereed

Keywords

  • Kernel regression
  • Multi-view learning
  • Spectral regression
  • Subclass discriminant analysis
  • Subspace learning

Publication forum classification

  • Publication forum level 3

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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