Graph-embedded subspace support vector data description

Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

3 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli108999
Sivumäärä13
JulkaisuPattern Recognition
Vuosikerta133
Varhainen verkossa julkaisun päivämäärä2022
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

  • Jufo-taso 3

!!ASJC Scopus subject areas

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

Sormenjälki

Sukella tutkimusaiheisiin 'Graph-embedded subspace support vector data description'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä