An Analysis of Inference with the Universum
Olivier Chapelle, Alekh Agarwal, Fabian H. Sinz, Bernhard Schölkopf · Neural Information Processing Systems · 2007
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.