Rapid and brief communication: Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion

Authors:
A. K. Qin;P. N. Suganthan;M. Loog
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Block S2, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Block S2, Singapore 639798, Singapore;Department of Innovation, Image Analysis Group, IT University of Copenhagen, Rued Langgards Vej 7, DK-2300 Copenhagen, Denmark
Venue:
Pattern Recognition
Year:
2005

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Abstract

We propose an uncorrelated heteroscedastic LDA (UHLDA) technique, which extends the uncorrelated LDA (ULDA) technique by integrating the weighted pairwise Chernoff criterion. The UHLDA can extract discriminatory information present in both the differences between per class means and the differences between per class covariance matrices. Meanwhile, the extracted feature components are statistically uncorrelated the maximum number of which exceeds the limitation of the ULDA. Experimental results demonstrate the promising performance of our proposed technique compared with the ULDA.