PERFORMANCE OF LINEAR DISCRIMINANT ANALYSIS IN STOCHASTIC SETTINGS Conference Paper uri icon

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. © 2013 IEEE.

author list (cited authors)

  • Zollanvari, A., Hua, J., & Dougherty, E. R.

citation count

  • 0

publication date

  • May 2013

publisher