Graph-embedded subspace support vector data description

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Abstract

In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-class techniques as its special cases and provides further insight on what these techniques actually optimize. The framework allows to incorporate other meaningful optimization goals via the graph preserving criterion and reveals a spectral solution and a spectral regression-based solution as alternatives to the previously used gradient-based technique. We combine the subspace learning framework iteratively with Support Vector Data Description applied in the subspace to formulate Graph-Embedded Subspace Support Vector Data Description. We experimentally analyzed the performance of newly proposed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification.

Original languageEnglish
Article number108999
Number of pages13
JournalPattern Recognition
Volume133
Early online date2022
DOIs
Publication statusPublished - 2023
Publication typeA1 Journal article-refereed

Keywords

  • One-Class classification
  • Spectral regression
  • Subspace learning
  • Support vector data description

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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