Fuzzy algorithms for learning vector quantization
Fuzzy algorithms for learning vector quantization
复制标题
用于学习矢量量化的模糊算法
DOI：
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
Pin
中科院分区：
文献类型：
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作者：
N. Karayiannis;Pin
本文介绍了学习矢量量化的模糊算法的发展。这些算法是通过最小化代表特征向量的输入向量和代表原型的竞争学习向量量化(LVQ)网络的权重向量之间的欧几里得距离的平方和而得到的。这种形式导致了竞争性算法，允许每个输入向量吸引所有原型。每个输入和原型之间的吸引力强度由一组隶属函数确定，该隶属函数可以根据特定的标准进行选择。对于一类满足某些性质的可容许隶属函数，给出了一种基于梯度下降的学习规则。通过选择具有不同性质的可容许隶属函数，发展了FALVQ1、FALVQ2和FALVQ3族算法。使用IRIS数据集对所提出的算法进行了测试和评估。在基于矢量量化的图像压缩所需的码本设计中的应用也证明了所提算法的有效性。
This paper presents the development of fuzzy algorithms for learning vector 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 represent the prototypes. This formulation leads to competitive algorithms, which allow each input vector to attract all prototypes. The strength of attraction between each input and the prototypes is determined by a set of membership functions, which can be selected on the basis of specific criteria. A gradient-descent-based learning rule is derived for a general class of admissible membership functions which satisfy certain properties. The FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithms are developed by selecting admissible membership functions with different properties. The proposed algorithms are tested and evaluated using the IRIS data set. The efficiency of the proposed algorithms is also illustrated by their use in codebook design required for image compression based on vector quantization.