Aarhus University Seal

Graph-embedded subspace support vector data description

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Fahad Sohrab, Tampere University
  • ,
  • Alexandros Iosifidis
  • Moncef Gabbouj, Tampere University
  • ,
  • Jenni Raitoharju, Finnish Environment Institute, University of Jyväskylä

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
JournalPattern Recognition
Publication statusPublished - Jan 2023

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

    Research areas

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

See relations at Aarhus University Citationformats

ID: 317374028