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SVDD-Based Pattern Denoising

parkj{at}korea.ac.kr Department of Control and Instrumentation Engineering, Korea University, Jochiwon, Chungnam, 339-700, Korea

Daesung Kang

mpkds{at}korea.ac.kr Department of Control and Instrumentation Engineering, Korea University, Jochiwon, Chungnam, 339-700, Korea

Jongho Kim

jongho6270.kim{at}samsung.com Mechatronics and Manufacturing Technology Center, Samsung Electronics Co., Ltd., Suwon, Gyeonggi, 443-742, Korea

James T. Kwok

jamesk{at}cse.ust.hk Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

Ivor W. Tsang

ivor{at}cse.ust.hk Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

The support vector data description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using 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 letter is to extend the main idea of SVDD to pattern denoising. Combining the geodesic projection to the spherical decision boundary resulting from the SVDD, together with solving the preimage problem, we propose a new method for pattern denoising. We first solve SVDD for the training data and then for each noisy test pattern, obtain its denoised feature by moving its feature vector along the geodesic on the manifold to the nearest decision boundary of the SVDD ball. Finally we find the location of the denoised pattern by obtaining the pre-image of the denoised feature. The applicability of the proposed method is illustrated by a number of toy and real-world data sets.