A Note on Learning Vector Quantization

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.

Authors

• Virginia de
• Dana Ballard

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