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

Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

1 Sitaatiot (Scopus)
1 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli107660
JulkaisuPattern Recognition
Vuosikerta111
Varhainen verkossa julkaisun päivämäärä19 syysk. 2020
DOI - pysyväislinkit
TilaJulkaistu - maalisk. 2021
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

  • Jufo-taso 3

!!ASJC Scopus subject areas

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

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