Unsupervised Parallel Feature Extraction from First Principles

We describe a number of learning rules that can be used to train un(cid:173) supervised parallel feature extraction systems. The learning rules are derived using gradient ascent of a quality function. We con(cid:173) sider a number of quality functions that are rational functions of higher order moments of the extracted feature values. We show that one system learns the principle components of the correla(cid:173) tion matrix. Principal component analysis systems are usually not optimal feature extractors for classification.