Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion

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.