title: Estimating the support of a high-dimensional distribution.
abstract: Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.
authors: Microsoft Research Ltd, Cambridge CB2 3NH, U.K.
authors: B
authors: B
authors: Schölkopf
authors: J C
authors: JC
authors: Platt
authors: J
authors: J
authors: Shawe-Taylor
authors: A J
authors: AJ
authors: Smola
authors: R C
authors: RC
authors: Williamson