Pattern De-Noising Based on Support Vector Data Description

Jooyoung Park, Daesung Kang, Jongho Kim, James T. Kwok, Ivor W. Tsang.

Abstract: The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to extend the main idea of the SVDD for the problem of pattern de-noising. Combining the projection onto the spherical decision boundary resulting from the SVDD together with a solver for the pre-image problem, we propose a new method for pattern de-noising. In the proposed method, we first solve the SVDD for the training data, then for each noisy test pattern, perform de-noising by projecting its feature vector onto the decision boundary on the feature space, and finally find the location of the de-noised pattern by obtaining the pre-image of the projection. The applicability of the proposed method is illustrated via an example dealing with noisy handwritten digits.

Proceedings of the International Joint Conference on Neural Networks (IJCNN'05), pp.949-953, Montreal, Canada, July 2005.


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