A Note on Learning Vector Quantization

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Virginia de, Dana Ballard


Vector Quantization is useful for data compression. Competitive Learn(cid:173) ing which minimizes reconstruction error is an appropriate algorithm for vector quantization of unlabelled data. Vector quantization of labelled data for classification has a different objective, to minimize the number of misclassifications, and a different algorithm is appropriate. We show that a variant of Kohonen's LVQ2.1 algorithm can be seen as a multi(cid:173) class extension of an algorithm which in a restricted 2 class case can be proven to converge to the Bayes optimal classification boundary. We compare the performance of the LVQ2.1 algorithm to that of a modified version having a decreasing window and normalized step size, on a ten class vowel classification problem.