Asymmetric Principal Component and Discriminant Analyses for Pattern Classification

Authors:
Xudong Jiang
Affiliations:
Nanyang Technological University, Singapore
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

Citing 0
Cited 13

Prediction of eigenvalues and regularization of eigenfeatures for human face verification

Pattern Recognition Letters
An asymmetric classifier based on partial least squares

Pattern Recognition
Unified formulation of linear discriminant analysis methods and optimal parameter selection

Pattern Recognition
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches

Decision Support Systems
Augmented DT-CWT feature based classification using Regularized Neighborhood Projection Discriminant Analysis for face recognition

Pattern Recognition
Face recognition based on the multi-scale local image structures

Pattern Recognition
Extract minimum positive and maximum negative features for imbalanced binary classification

Pattern Recognition
Directional two-dimensional principal component analysis for face recognition

Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices

Pattern Recognition
Simultaneous discriminative projection and dictionary learning for sparse representation based classification

Pattern Recognition
A complete and fully automated face verification system on mobile devices

Pattern Recognition
Bayesian predictive kernel discriminant analysis

Pattern Recognition Letters
Reconstruction of occluded facial images using asymmetrical Principal Component Analysis

Integrated Computer-Aided Engineering

Quantified Score

Hi-index	0.14

Visualization

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.