An Adaptive Learning and Self-Deleting Neural Network for Vector Quantization

概要

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This paper describes an adaptive neural vector quantization algorithm with a deleting approach of weight (reference) vectors. We call the algorithm an "adaptive learning and self-deleting" algorithm. At the beginning, we introduce an im-proved topological neighborhood and an adaptive vector quantization algorithm with little depending on initial values of weight vectors. Then we present the adaptive learning and self-deleting algorithm. The algorithm is represented as the following descriptions: At first, many weight vectors are prepared, and the algorithm is processed with Kohonen's self-organizing feature map.Next, weight vectors are deleted sequentially to the fixed number of them, and the algorithm processed with competitive 1earning.At the end, we discuss algorithms with neighborhood relations compared with the proposed one. The proposed a1gorithm is also good in the case of a poor initialization of weight vectors.Experimental results are given to show the effectiveness of the proposed algorithm.
社団法人電子情報通信学会の論文
1996-11-25