A Direct Kernel Uncorrelated Discriminant Analysis Algorithm

In this letter, we present a new formulation for uncorrelated discriminant analysis (UDA) in some high-dimensional feature space and then propose an efficient UDA algorithm using kernel technique. Unlike some existing UDA algorithms, which solve uncorrelated discriminant vectors one at a time, the proposed algorithm is able to extract all the uncorrelated discriminant vectors simultaneously in the feature space and does not suffer the small sample size problem. Experimental results show that the proposed method is very competitive in comparison with some existing discriminant analysis algorithms, in terms of recognition rate and robustness with respect to kernel parameters.