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 univa...View moreMetadata
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.
Date of Conference: 26-31 May 2013
Date Added to IEEE Xplore: 21 October 2013
Electronic ISBN:978-1-4799-0356-6