FUZZY ALGORITHMS FOR LEARNING VECTOR QUANTIZATION

Citation
Nb. Karayiannis et Pi. Pai, FUZZY ALGORITHMS FOR LEARNING VECTOR QUANTIZATION, IEEE transactions on neural networks, 7(5), 1996, pp. 1196-1211
Citations number
21
Categorie Soggetti
Computer Application, Chemistry & Engineering","Engineering, Eletrical & Electronic","Computer Science Artificial Intelligence","Computer Science Hardware & Architecture","Computer Science Theory & Methods
ISSN journal
10459227
Volume
7
Issue
5
Year of publication
1996
Pages
1196 - 1211
Database
ISI
SICI code
1045-9227(1996)7:5<1196:FAFLVQ>2.0.ZU;2-9
Abstract
This paper presents the development of fuzzy algorithms for learning v ector quantization (FALVQ). These algorithms are derived by minimizing the weighted sum of the squared Euclidean distances between an input vector, which represents a feature vector, and the weight vectors of a competitive learning vector quantization (LVQ) network, which represe nt the prototypes. This formulation Leads to competitive algorithms, w hich allow each input vector to attract all prototypes. The strength o f attraction between each input and the prototypes is determined by a set of membership functions, which can be selected on the basis of spe cific criteria, A gradient-descent-based learning rule is derived for a general class of admissible membership functions which satisfy certa in properties. The FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithm s are developed by selecting admissible membership functions with diff erent properties, The proposed algorithms are tested and evaluated usi ng the IRIS data set, The efficiency of the proposed algorithms is als o illustrated by their use in codebook design required for image compr ession based on vector quantization.