Unsupervised Parallel Feature Extraction from First Principles

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Mats Österberg, Reiner Lenz


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. Therefore we design quality functions which produce feature vectors that support unsu(cid:173) pervised classification. The properties of the different systems are compared with the help of different artificially designed datasets and a database consisting of all Munsell color spectra.