DocumentCode :
2773146
Title :
Maximum Margin Clustering on Data Manifolds
Author :
Wang, Fei ; Wang, Xin ; Li, Tao
Author_Institution :
Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL, USA
fYear :
2009
fDate :
6-9 Dec. 2009
Firstpage :
1028
Lastpage :
1033
Abstract :
Clustering is one of the most fundamental and important problems in computer vision and pattern recognition communities. Maximum margin clustering (MMC) is a recently proposed clustering technique which has shown promising experimental results. The main theme behind MMC is to extend the standard maximum margin principle in support vector machine (SVM) to the unsupervised scenario. This paper will consider the problem of maximum margin clustering on data manifolds. Specifically, we propose an approach called manifold regularized maximum margin clustering (MRMMC) which combines both the maximum margin data discrimination and data manifold information in a unified clustering objective and propose an efficient algorithm to solve it. Finally the experimental results on several real world data sets are presented to show the effectiveness of our method.
Keywords :
pattern clustering; support vector machines; computer vision; data manifolds; manifold regularized maximum margin clustering; maximum margin data discrimination; pattern recognition; support vector machine; Clustering algorithms; Clustering methods; Communities; Computer vision; Data mining; Face recognition; Manifolds; Pattern recognition; Stereo vision; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on
Conference_Location :
Miami, FL
ISSN :
1550-4786
Print_ISBN :
978-1-4244-5242-2
Electronic_ISBN :
1550-4786
Type :
conf
DOI :
10.1109/ICDM.2009.104
Filename :
5360351
Link To Document :
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