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Support vector components analysis

In this paper we propose a novel method for learning a distance metric in the process of training Support Vector Machines (SVMs) with the radial basis function kernel. A transformation matrix is adapted in such a way that the SVM dual objective of a classification problem is optimized. By using a wide transformation matrix the method can effectively be used as a means of supervised dimensionality reduction. We compare our method with other algorithms on a toy dataset and on PET-scans of patients with various Parkinsonisms, finding that our method either outperforms or performs on par with the other algorithms.