Fuzzy-Kernel Learning Vector Quantization

This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the wellknown fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results show that FKLVQ is more accurate and needs far fewer iteration steps than the latter two algorithms. Moreover FKLVQ shows good robustness to outliers.

• Type: Conference
• Year: 2004
• Where: ISNN
• Authors: Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou
• Keywords: Fuzzy Membership Functions, ISNN 2004, Learning Vector Quantization, Wellknown Fuzzy Lvq