Performance of linear discriminant analysis in stochastic settings

Amin Zollanvari, Jianping Hua, Edward R. Dougherty

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

Abstract

This paper provides, for the first time, exact analytical expressions for the first moment of the true error of linear discriminant analysis (LDA) when the data are univariate and taken from two stochastic Gaussian processes. We assume a general setting in which the sample data from each class do not need to be identically distributed or independent within or between classes. As an application of this framework, we characterize the performance of LDA in situations that the data are generated from autoregressive models of the first order.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages3437-3441
Number of pages5
DOIs
Publication statusPublished - Oct 18 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Expected error
  • Gaussian processes
  • Linear discriminant analysis
  • Non-i.i.d data
  • Stochastic settings

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Zollanvari, A., Hua, J., & Dougherty, E. R. (2013). Performance of linear discriminant analysis in stochastic settings. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 3437-3441). [6638296] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638296