Growing graph network based on an online gaussian mixture model

Abstract

In this paper, the author proposes a growing neural network based on an online Gaussian mixture model, in which mechanisms are included for growing Gaussian kernels and finding topologies between kernels using graph paths. The proposed method has the following advantages compared with conventional growing neural networks: no permanent increase in nodes (Gaussian kernels), robustness to noise, and increased speed of constructing networks. This paper presents the theory and algorithm for the proposed method and the results of verification experiments using artificial data. © 2011 Springer-Verlag Berlin Heidelberg.