Construction of Self-Organizing Neural Gas Networks

Abstract

Vector quantization have been used for both storage and transmission of speech and image data, and often requirs the algorithm that minimizes the distortion error. To obtain the minimum distortion error in the neural networks for vector quantization, reformatory competitive learnings and so on., have been introduced. Among the number of algorithms, neural gas networks are well known for showing better performance. In this paper, we propose some self-organizing neural gas networks, self-deleting neural gas networks and ones which are combinations of them. The conventional and proposed methods are compared by the tasks to compress image data. It is shown that the method which is a combination of deleting and creating is more effective than the other algorithms.