Application of double asymptotics and random matrix theory in error estimation of regularized linear discriminant analysis

Amin Zollanvari, Edward R. Dougherty

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

2 Citations (Scopus)

Abstract

The theory of double asymptotics and random matrices has been employed to construct a nearly unbiased estimator of true error rate of linear discriminant analysis with ridge estimator of inverse covariance matrix in the multivariate Gaussian model. In such a scenario, the performance of the constructed estimator, as measured by Root-Mean-Square (RMS) error, shows improvement over well-known estimators of true error.

Original languageEnglish
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages57-59
Number of pages3
DOIs
Publication statusPublished - Dec 1 2013
Externally publishedYes
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

Keywords

  • Double asymptotics
  • Kolmogorov asymptotics
  • Linear discriminant analysis
  • Random matrix theory
  • Small-Sample

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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