An Analysis of Inference with the Universum

Sinz, FH; Chapelle, O; Agarwal, A; Schölkopf, B

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An Analysis of Inference with the Universum

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Sinz, FH
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Sinz, F., Chapelle, O., Agarwal, A., & Schölkopf, B. (2008). An Analysis of Inference with the Universum. In C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007 (pp. 1369-1376). Red Hook, NY, USA: Curran.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C727-4

Abstract

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
projected subspace (or, equivalently, with a data-dependent reduced kernel). We also provide experimental results.