A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure
Document Type
Article
Publication Date
1-1-2019
Abstract
This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments. © 2019 World Scientific Publishing Company.
Keywords
adaptive resonance theory, kernel Bayes rule, topology construction, Unsupervised clustering
Divisions
ai
Funders
Frontier Research Grant (Project No. FG003-17AFR) from University of Malaya,ONRG grant (Project No: ONRG-NICOP-N62909-18-1-2086) from office of Naval Research Global, UK
Publication Title
International Journal of Neural Systems
Volume
29
Issue
05
Publisher
World Scientific Publishing