A Revisit to Support Vector Data Description
نویسندگان
چکیده
Support vector data description (SVDD) is a useful method for outlier detection and has been applied to a variety of applications. However, in the existing optimization procedure of SVDD, there are some issues which may lead to improper usage of SVDD. Some of the issues might already be known in practice, but the theoretical discussion, justification and correction are still lacking. Given the wide use of SVDD, these issues inspire us to carefully study SVDD in the view of convex optimization. In particular, we derive the dual problem with strong duality, prove theorems to handle theoretical insufficiency in the literature of SVDD, investigate some novel extensions of SVDD, and come up with an implementation of training SVDD with theoretical guarantee.
منابع مشابه
A Revisit to Support Vector Data Description (SVDD)
Support vector data description (SVDD), proposed by [1], is a useful method for outlier detection. Its model is obtained by solving the dual optimization problem. In this paper, we point out some issues in their derivations. For example, they formulate SVDD as a non-convex problem and derive the dual problem only under some parameter settings. Given the wide use of SVDD, it is important to addr...
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