Unsupervised Matrix-valued Kernel Learning For One Class Classification

This paper is concerned with the one class classification(OCC) problem. By introducing the vector-valued function with regularizations in Y-valued Reproducing Hilbert Kernel Space(RHKS), we build an unsupervised classifier and discover the outliers and inliers simultaneously. Manifold regularization is employed to preserve the local similarity of data in input space. Experimental results of the proposed and comparing methods on OCC data sets demonstrate the performance of the proposed algorithm.

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