Analytical Study of Performance of Linear Discriminant Analysis in Stochastic Settings. Academic Article

abstract

• This paper provides exact analytical expressions for the first and second moments of the true error for linear discriminant analysis (LDA) when the data are univariate and taken from two stochastic Gaussian processes. The key point is that 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. We compare the true errors of designed classifiers under the typical i.i.d. model and when the data are correlated, providing exact expressions and demonstrating that, depending on the covariance structure, correlated data can result in classifiers with either greater error or less error than when training with uncorrelated data. The general theory is applied to autoregressive and moving-average models of the first order, and it is demonstrated using real genomic data.

authors

• Dougherty, Edward

published proceedings

• Pattern Recognit

author list (cited authors)

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

citation count

• 9

complete list of authors

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

publication date

• November 2013

publisher

• Elsevier  Publisher

published in

• Pattern Recognition  Journal

keywords

• Auto-regressive Models
• Correlated Data
• Expected Error
• Gaussian Processes
• Linear Discriminant Analysis
• Moving-average Models
• Non-i.i.d Data
• Stochastic Settings

PubMed Central ID

• 24039299

Digital Object Identifier (DOI)

• 10.1016/j.patcog.2013.04.002

start page

• 3017

end page

• 3029

volume

• 46

issue

• 11

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.patcog.2013.04.002