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Conference papers

Kernel-based unsupervised trajectory clusters discovery

Abstract : Nowadays support vector machines (SVM) are among the most popular tools for data clustering. Even though the basic SVM technique works only for 2-classes problems, in the last years many variants of the original approach have been proposed, such as multi-class SVM for multiple class problems and single-class SVM for outlier detection. However, the former is based on a supervised approach, and the number of classes must be known a-priori; the latter performs unsupervised learning, but it can only discriminate between normal and outlier data. In this paper we propose a novel technique for data clustering when the number of classes is unknown. The proposed approach is inspired by single-class SVM theory and exploits some geometrical properties of the feature space of Gaussian kernels. Experimental results are given with special focus on the field of trajectory clustering.
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Contributor : Peter Sturm Connect in order to contact the contributor
Submitted on : Monday, September 29, 2008 - 6:14:48 PM
Last modification on : Monday, September 29, 2008 - 8:21:21 PM
Long-term archiving on: : Friday, June 4, 2010 - 11:57:59 AM


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  • HAL Id : inria-00325650, version 1



C. Piciarelli, C. Micheloni, Gian Luca Foresti. Kernel-based unsupervised trajectory clusters discovery. The Eighth International Workshop on Visual Surveillance - VS2008, Graeme Jones and Tieniu Tan and Steve Maybank and Dimitrios Makris, Oct 2008, Marseille, France. ⟨inria-00325650⟩



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