Clustering in image space in support vector machine

The kernel-based clustering has attracted great attention with the development of support vector machine. One can perform a clustering approach in an image space after mapping the data in an original space to the image space, but it is difficult to capture the optimal parameters for finding real clu...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 1021 - 1025
Main Authors Shi-Hong Yue, Kai Zhang, Wei-Xia Liu, Yan-Min Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Automation
Cybernetics
Data structures
Euclidean distance
Kernel
Machine learning
Prototypes
Risk management
Support vector machine classification
Support vector machines
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Summary:The kernel-based clustering has attracted great attention with the development of support vector machine. One can perform a clustering approach in an image space after mapping the data in an original space to the image space, but it is difficult to capture the optimal parameters for finding real clusters. In this paper, we present a kernel-based clustering approach in light of a relational fuzzy clustering procedure. This approach offers a better solution to the kernel-based clustering compared with conventional approaches. Experiments are presented to demonstrate the effectiveness of our proposed method.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212401