COMMENTS ON LINEAR FEATURE EXTRACTION.
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TL;DR
A compromise solution is obtained for the case in which the data has both different means and different covariances under the alternative hypotheses and the Bhattacharya distance is used to measure the information carried by the transformed data.
Abstract
Abstract : The problem considered is that of finding the best linear transformation to reduce a random data vector z to vector of smaller dimension. It is assumed that the original data are Gaussian under either of two hypotheses. The Bhattacharya distance is used to measure the information carried by the transformed data. A compromise solution is obtained for the case in which the data has both different means and different covariances under the alternative hypotheses. (Author)