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Eigenvalue based spectral classification

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Listed:
  • Piotr Borkowski
  • Mieczysław A Kłopotek
  • Bartłomiej Starosta
  • Sławomir T Wierzchoń
  • Marcin Sydow

Abstract

This paper describes a new method of classification based on spectral analysis. The motivations behind developing the new model were the failures of the classical spectral cluster analysis based on combinatorial and normalized Laplacian for a set of real-world datasets of textual documents. Reasons of the failures are analysed. While the known methods are all based on usage of eigenvectors of graph Laplacians, a new classification method based on eigenvalues of graph Laplacians is proposed and studied.

Suggested Citation

  • Piotr Borkowski & Mieczysław A Kłopotek & Bartłomiej Starosta & Sławomir T Wierzchoń & Marcin Sydow, 2023. "Eigenvalue based spectral classification," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-35, April.
    • Handle: RePEc:plo:pone00:0283413
    DOI: 10.1371/journal.pone.0283413
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    References listed on IDEAS

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    1. Petros Xanthopoulos & Mario Guarracino & Panos Pardalos, 2014. "Robust generalized eigenvalue classifier with ellipsoidal uncertainty," Annals of Operations Research, Springer, vol. 216(1), pages 327-342, May.
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