Regression Learning Vector Quantization

Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used because of their intuitively clear learning process and ease of implementation. In this paper we propose an extension of the LVQ algorithm to regression. Just like the LVQ algorithm, the proposed modification uses a supervised learning procedure to learn the best prototype positions, but unlike LVQ algorithm for classification, it also learns the best prototype target values. This results in the effective partition of the feature space, similar to the one the K-means algorithm would make. Experimental results on benchmark datasets showed that the proposed Regression LVQ algorithm performs better than the nearest prototype competitors that choose prototypes randomly or through K-means clustering, classification LVQ on quantized target values, and similarly to the memory-based Parzen Window and Nearest Neighbor algorithms.