It is revealed that there is a link between the quantization approach employed and the dimension of the vector parameter which can be accurately estimated by a quantized estimation system. A critical quantity called inestimable dimension for quantized data (IDQD) is introduced, which doesn’t depend on the quantization regions and the statistical models of the observations but instead depends on...

We apply the cross-entropy (CE) method to problems in clustering and vector quantization. The CE algorithm involves the following iterative steps: (a) the generation of clusters according to a certain parametric probability distribution, (b) updating the parameters of this distribution according to the Kullback-Leibler cross-entropy. Through various numerical experiments we demonstrate the high...

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 a...