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
Publication Title
International Journal of Neural Systems
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
Volume
29
Issue
05
Publisher
World Scientific Publishing