Mutual information feature extractors for neural classifiers

Presents and evaluates two linear feature extractors based on mutual information. These feature extractors consider general dependencies between features and class labels, as opposed to statistical techniques such as PCA which does not consider class labels and LDA, which uses only simple first order dependencies. As evidenced by several simulations on high dimensional data sets, the proposed techniques provide superior feature extraction and better dimensionality reduction while having similar computational requirements