PERFORMANCE OF LINEAR DISCRIMINANT ANALYSIS IN STOCHASTIC SETTINGS Conference Paper

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.

name of conference

• 2013 IEEE International Conference on Acoustics, Speech and Signal Processing

authors

• Dougherty, Edward

published proceedings

• 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)

author list (cited authors)

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

citation count

• 0

complete list of authors

• Zollanvari, Amin||Hua, Jianping||Dougherty, Edward R

publication date

• May 2013

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference)  Journal

keywords

• Expected Error
• Gaussian Processes
• Linear Discriminant Analysis
• Non-i.i.d Data
• Stochastic Settings

Digital Object Identifier (DOI)

• 10.1109/icassp.2013.6638296

International Standard Book Number (ISBN) 13

• 9781479903566

start page

• 3437

end page

• 3441

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Ficassp.2013.6638296