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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| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers
2019
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/24041/ https://doi.org/10.1109/ACCESS.2019.2921832 |
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| Summary: | 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 measure, a node insertion criterion, and an edge creation criterion. Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. Moreover, generated topological networks cannot represent the distribution of data. In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. Furthermore, improving the ability to express the data structure more appropriately by the topological network, a mechanism that adaptively controls the node insertion criterion is introduced to the proposed algorithm. The experimental results showed that the proposed algorithm has superior performance with respect to the self-organizing and the classification abilities compared with the state-of-the-art topological clustering algorithms. © 2013 IEEE. |
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