A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

Masuyama, Naoki and Loo, Chu Kiong and Wermter, Stefan (2019) A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure. International Journal of Neural Systems, 29 (05). p. 1850052. ISSN 0129-0657, DOI https://doi.org/10.1142/S0129065718500521.

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Official URL: https://doi.org/10.1142/S0129065718500521


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.

Item Type: Article
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
Uncontrolled Keywords: adaptive resonance theory; kernel Bayes rule; topology construction; Unsupervised clustering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 19 Mar 2020 03:50
Last Modified: 19 Mar 2020 03:50
URI: http://eprints.um.edu.my/id/eprint/24040

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