Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). Specifically, the proposed algorithm utilizes the correntropy induced metric (CIM) for defining a similarity measur...

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Bibliographic Details
Main Authors: Naoki Masuyama, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota, Yusuke Nojima, Yiping Liu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Adaptive resonance theory
correntropy
information theoretic learning
topological clustering
Online Access:https://ieeexplore.ieee.org/document/8733796/

Internet

https://ieeexplore.ieee.org/document/8733796/

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