CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.05 - May
Issue No.05 - May (2009 vol.31)
Xudong Jiang , Nanyang Technological University, Singapore
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.
Dimension reduction, feature extraction, principal component analysis, discriminant analysis, classification, face detection.
Xudong Jiang, "Asymmetric Principal Component and Discriminant Analyses for Pattern Classification", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 5, pp. 931-937,, May 2009, doi:10.1109/TPAMI.2008.258