Soft learning vector quantization

James C. Bezdek; Nikhil R. Pal

doi:10.1117/12.211799

13 June 1995 Soft learning vector quantization

James C. Bezdek, Nikhil R. Pal

Proceedings Volume 2493, Applications of Fuzzy Logic Technology II; (1995) https://doi.org/10.1117/12.211799
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Learning vector quantization (LVQ) often requires extensive experimentation with the learning rate distribution and update neighborhood used during iteration towards good prototypes. A single winner prototype controls the updates. This paper discusses two soft relatives of LVQ: the soft competition scheme (SCS) of Yair et al. and fuzzy LVQ equals FLVQ. These algorithms both extend the update rates that are partially based on posterior probabilities. FLVQ is a batch algorithm whose learning rates are derived from fuzzy memberships. We show several relationships between SCS and FLVQ; and we show that SCS learning rates can be interpreted in terms of statistical decision theory. Finally, we show the relationship between FLVQ, fuzzy c-means, hard c-means, a batch version of LVQ, and SCS.

Citation Download Citation

James C. Bezdek and Nikhil R. Pal "Soft learning vector quantization", Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); https://doi.org/10.1117/12.211799

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