@techreport{platt1999estimating,
author = {Platt, John and Schölkopf, Bernhard and Shawe-Taylor, John and Smola, Alex J. and Williamson, Robert C.},
title = {Estimating the Support of a High-Dimensional Distribution},
year = {1999},
month = {November},
abstract = {Suppose you are given some dataset 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 is bounded by some a priori specified v between 0 and 1. We propose a method to approach this problem by trying to estimate a function f which 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 preliminary theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data.},
url = {http://approjects.co.za/?big=en-us/research/publication/estimating-the-support-of-a-high-dimensional-distribution/},
pages = {30},
number = {MSR-TR-99-87},
}