Asymmetric principal component and discriminant analyses for pattern classification.

Full Abstract

This paper studies the roles of the principal component and discriminant analyses in the pattern classification and explores their problems with the asymmetric classes and/or the unbalanced training data. An asymmetric principal component analysis (APCA) is proposed to remove the unreliable dimensions more effectively than the conventional PCA. Targeted at the two-class problem, an asymmetric discriminant analysis in the APCA subspace is proposed to regularize the eigenvalue that is, in general, a biased estimate of the variance in the corresponding dimension. These efforts facilitate a reliable and discriminative feature extraction for the asymmetric classes and/or the unbalanced training data. The proposed approach is validated in the experiments by comparing it with the related methods. It consistently achieves the highest classification accuracy among all tested methods in the experiments.

Author information

Author/s: Jiang, Xudong (X);

Affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Link, Singapore. exdjiang(-atsign-)tu.edu.sg

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2009-May; vol 31 (issue 5) : pp 931-7

Dates: Created 2009/05/14; Completed 2009/06/10;

PMID: 19441177, status: MEDLINE (last retrieval date: 6/10/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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