Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric

被引：0

作者：

Masuyama, Naoki ^{[1
]}

Amako, Narito ^{[1
]}

Nojima, Yusuke ^{[1
]}

Liu, Yiping ^{[1
]}

Loo, Chu Kiong ^{[2
]}

Ishibuchi, Hisao ^{[3
]}

机构：

[1] Osaka Prefecture Univ, Grad Sch Engn, Naka Ku, Gakuen Cho, Sakai, Osaka 5998531, Japan

[2] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia

[3] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Univ Key Lab Evolving Intelligent Syst Guangdong, Shenzhen 518055, Peoples R China

来源：

2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

adaptive resonance theory; clustering; correntropy induced metric;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Adaptive Resonance Theory (ART)-based growing self-organizing clustering is one of the most promising approaches for unsupervised topological clustering. In our previous study, we proposed a Topological Correntropy induced metric based ART (TCA) and shown its superior performance. However, TCA suffers from a data-dependent parameter and a complicated network creation process which lead to inefficient learning. This paper aims to solve problems of TCA by implementing an automatic parameter specification mechanism and simplifying a learning algorithm. Experimental results show that the proposed algorithm in this paper successfully solved the above problems.

引用

页码：2215 / 2221

页数：7

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