Abstract
One-class Support Vector Machine (OCSVM) is an effective method in researching of one-class classification problems, which extracts a hyperplane in a kernel feature space such that a given fraction of training objects may reside beyond the hyperplane, while the hyperplane has maximal distance to the origin. However, OCSVM is generally based on vector pattern, hence, when the input of the classifier is a non-vector pattern, such as a face image, it has to be concatenated to construct a vector firstly. In this paper, inspired by 2D feature extractions and 2D classifier designs, we develop a new OCSVM based on matrix patterns, called MatOCSVM. Experimental results on ORL face database and Letter text-base show that the proposed method is competitive in one-class classification performance compared to OCSVM.
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© 2011 Springer-Verlag Berlin Heidelberg
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Yan, Y., Wang, Q., Ni, G., Pan, Z., Kong, R. (2011). One-Class Support Vector Machines Based on Matrix Patterns. In: Jiang, L. (eds) Proceedings of the 2011, International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19–20, 2011, Melbourne, Australia. Advances in Intelligent and Soft Computing, vol 111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25188-7_27
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DOI: https://doi.org/10.1007/978-3-642-25188-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25187-0
Online ISBN: 978-3-642-25188-7
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