Alternative learning vector quantization

In this paper, we discuss the influence of feature vectors contributions at each learning time t on a sequential-type competitive learning algorithm. We then give a learning rate annealing schedule to improve the unsupervised teaming vector quantization (ULVQ) algorithm which uses the winner-take-all competitive learning principle in the self-organizing map (SOM). We also discuss the noisy and outlying problems of a sequential competitive learning algorithm and then propose an alternative learning formula to make the sequential competitive teaming robust to noise and outliers. Combining the proposed learning rate annealing schedule and alternative teaming formula, we propose an alternative learning vector quantization (ALVQ) algorithm. Some discussion and experimental results from comparing ALVQ with ULVQ show the superiority of the proposed method. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.