Linear vector classification: An improvement on LVQ algorithms to create classes of patterns

Abstract

Learning Vector Quantization algorithms (LVQ1 and LVQ2), proposed by Kohonen, are widely used for the quantization and the classification of vectors into clusters. These algorithms quantize each class of vectors in the space into a defined number of ‘prototypes’. Despite an efficient quantization of the stimuli space, these algorithms are not well adapted to classification tasks where the distribution of prototypes inside a single class is not important, provided that the boundaries between classes are adequately approximated through the prototypes. We propose here an adaptation of the LVQ1 algorithm where the resulting prototypes will approximate the boundaries between classes; by this way, stimuli located as well near the border as in the center of a class will be correctly classified, even if they are not adequately quantified in the sense of ‘Vector Quantization’.