Random matrix theory in pattern classification: An application to error estimation

Amin Zollanvari, Edward R. Dougherty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

We employed the Random Matrix Theory (RMT) to construct a nearly unbiased estimator of true error rate of linear discriminant analysis (LDA) with ridge estimator of inverse covariance matrix in the multivariate Gaussian model and in small-sample situation. In such a scenario, the performance of the constructed estimator, as measured by Root-Mean-Square (RMS) error, shows consistent improvement over well-known estimators of true error.

Original languageEnglish
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages884-887
Number of pages4
ISBN (Print)9781479923908
DOIs
Publication statusPublished - Jan 1 2013
Externally publishedYes
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

Keywords

  • Error Estimation
  • Linear Discriminant Analysis
  • Linear discriminant analysis
  • Small-Sample

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Zollanvari, A., & Dougherty, E. R. (2013). Random matrix theory in pattern classification: An application to error estimation. In Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers (pp. 884-887). [6810415] (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810415