PERFORMANCE OF LINEAR DISCRIMINANT ANALYSIS IN STOCHASTIC SETTINGS
Additional Document Info
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.
name of conference
2013 IEEE International Conference on Acoustics, Speech and Signal Processing