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Proceedings Paper

Local manifold spectral clustering with FCM data condensation
Author(s): Hanqiang Liu; Licheng Jiao; Feng Zhao
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Paper Abstract

In this paper, a novel local manifold spectral clustering with fuzzy c-means (FCM) data condensation is presented. Firstly, a multilayer FCM data condensation method is performed on the original data to contain a condensation subset. Secondly, the spectral clustering algorithm based on the local manifold distance measure is used to realize the classification of the condensation subset. Finally, the nearest neighbor method is adopted to obtain the clustering result of the original data. Compared with the standard spectral clustering algorithm, the novel method is more robust and has the advantages of effectively dealing with the large scale data. In our experiments, we first analyze the performances of multilayer FCM data condensation and local manifold distance measure, then apply our method to solve image segmentation and the large Brodatz texture images classification. The experimental results show that the method is effective and extensible, and especially the runtime of this method is acceptable.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961Y (30 October 2009); doi: 10.1117/12.832637
Show Author Affiliations
Hanqiang Liu, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)
Feng Zhao, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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