Relevance determination in Learning Vector Quantization

We propose a method to automatically determine the relevance of the input dimensions of a learning vector quantization (LVQ) architecture during training. The method is based on Hebbian learning and introduces weighting factors of the input dimensions which are automatically adapted to the speci c problem. The bene ts are twofold: On the one hand, the incorporation of relevance factors in the LVQ architecture increases the overall performance of the classi cation and adapts the metric to the speci c data used for training. On the other hand, the method induces a pruning algorithm, i.e. an automatic detection of the input dimensions which do not contribute to the overall classi er. Hence we obtain a possibly more eAEcient classi cation and we gain insight to the role of the data dimensions.