A Note on Learning Vector Quantization

Vector Quantization is useful for data compression. Competitive Learning 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 multiclass 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.

• Type: Conference
• Year: 1992
• Where: NIPS
• Authors: Virginia R. de Sa, Dana H. Ballard
• Keywords: Algorithm, Kohonen's LVQ2.1 Algorithm, NIPS 1992, NIPS 2007, Vector Quantization