An Analysis of Inference with the Universum

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Olivier Chapelle, Alekh Agarwal, Fabian Sinz, Bernhard Schölkopf


We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative data, a third class of data is available, termed the Universum. We assay the behavior of the algorithm by establishing links with Fisher discriminant analysis and oriented PCA, as well as with an SVM in a pro- jected subspace (or, equivalently, with a data-dependent reduced kernel). We also provide experimental results.