Alternative learning vector quantization

Authors:
Kuo-Lung Wu;Miin-Shen Yang
Affiliations:
Department of Information Management, Kun Shan University, Yung-Kang, Tainan 71023, Taiwan, ROC;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan, ROC
Venue:
Pattern Recognition
Year:
2006

Citing 21
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Abstract

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 learning 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 learning robust to noise and outliers. Combining the proposed learning rate annealing schedule and alternative learning 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.