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A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

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...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Masuyama, Naoki, Loo, Chu Kiong, Wermter, Stefan
التنسيق: مقال
منشور في: World Scientific Publishing 2019
الموضوعات:
QA75 Electronic computers. Computer science
الوصول للمادة أونلاين:http://eprints.um.edu.my/24040/
https://doi.org/10.1142/S0129065718500521
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id my.um.eprints.24040
record_format eprints
spelling my.um.eprints.240402020-03-19T03:50:02Z http://eprints.um.edu.my/24040/ A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure Masuyama, Naoki Loo, Chu Kiong Wermter, Stefan QA75 Electronic computers. Computer science 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. World Scientific Publishing 2019 Article PeerReviewed 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 https://doi.org/10.1142/S0129065718500521 doi:10.1142/S0129065718500521
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Masuyama, Naoki
Loo, Chu Kiong
Wermter, Stefan
A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure
description 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.
format Article
author Masuyama, Naoki
Loo, Chu Kiong
Wermter, Stefan
author_facet Masuyama, Naoki
Loo, Chu Kiong
Wermter, Stefan
author_sort Masuyama, Naoki
title A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure
title_short A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure
title_full A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure
title_fullStr A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure
title_full_unstemmed A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure
title_sort kernel bayesian adaptive resonance theory with a topological structure
publisher World Scientific Publishing
publishDate 2019
url http://eprints.um.edu.my/24040/
https://doi.org/10.1142/S0129065718500521
_version_ 1662755214507114496
score 13.251813

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