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Graph-based semi-supervised Support Vector Data Description for novelty detection | IEEE Conference Publication | IEEE Xplore

Graph-based semi-supervised Support Vector Data Description for novelty detection


Abstract:

Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labele...Show More

Abstract:

Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper, we propose to utilize the information implicated in a spectral graph to leverage SVDD in the context of semi-supervised learning. The theory and experiment evidence that the proposed method is able to efficiently employ the information carried in the spectral graph to not only enhance the generalization ability of SVDD but also enforce the cluster assumption which is crucial for a semi-supervised learning method.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
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Conference Location: Killarney, Ireland

References

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