- Title
- Support vector clustering through proximity graph modelling
- Creator
- Yang, Jianhua; Estivill-Castro, Vladimir; Chalup, Stephan K.
- Relation
- 9th International Conference on Neural Information Processing (ICONIP'02). Proceedings of the 9th International Conference on Neural Information Processing, 2002 (ICONIP'02), Volume 2 (Singapore 18-22 November, 2002) p. 898-903
- Publisher Link
- http://dx.doi.org/10.1109/ICONIP.2002.1198191
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2002
- Description
- Support vector machines (SVMs) have been widely adopted for classification, regression and novelty detection. Recent studies (A. Ben-Hur et al., 2001) proposed to employ them for cluster analysis too. The basis of this support vector clustering (SVC) is density estimation through SVM training. SVC is a boundary-based clustering method, where the support information is used to construct cluster boundaries. Despite its ability to deal with outliers, to handle high dimensional data and arbitrary boundaries in data space, there are two problems in the process of cluster labelling. The first problem is its low efficiency when the number of free support vectors increases. The other problem is that it sometimes produces false negatives. We propose a robust cluster assignment method that harvests clustering results efficiently. Our method uses proximity graphs to model the proximity structure of the data. We experimentally analyze and illustrate the performance of this new approach.
- Subject
- support vector machines; cluster analysis; SVM training; boundary-based clustering; robotics
- Identifier
- http://hdl.handle.net/1959.13/915946
- Identifier
- uon:7862
- Identifier
- ISBN:9810475241
- Rights
- Copyright © 2002 IEEE. Reprinted from Volume 2 of the Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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