Estimating the Support of a High-Dimensional Distribution

Estimating the Support of a High-Dimensional Distribution (1999)

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by Bernhard Schölkopf , John C. Platt , John Shawe-taylor , Alex J. Smola , Robert C. Williamson

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Datum	Value	Source
TITLE	Estimating the Support of a High-Dimensional Distribution	user correction - Legacy Corrections
AUTHOR NAME	Bernhard Schölkopf	user correction - Legacy Corrections
AUTHOR NAME	John C. Platt	user correction - Legacy Corrections
AUTHOR NAME	John Shawe-taylor	user correction - Legacy Corrections
AUTHOR AFFIL	y	user correction - Legacy Corrections
AUTHOR NAME	Alex J. Smola	user correction - Legacy Corrections
AUTHOR NAME	Robert C. Williamson	user correction - Legacy Corrections
AUTHOR AFFIL	; Microsoft Research Ltd; z; Microsoft Research, 1 Microsoft Way, Redmond, WA; y; Royal Holloway, University of London; x; Department of Engineering, Australian National University,; Microsoft Research; Microsoft Corporation	user correction - Legacy Corrections
AUTHOR ADDR	; 1 Guildhall Street, Cambridge CB2 3NH, UK; USA; Egham, UK; Canberra 0200, Australia; One Microsoft Way; Redmond, WA 98052	user correction - Legacy Corrections
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 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 d...	user correction - Legacy Corrections
YEAR	1999	user correction - Legacy Corrections
CITATIONS	41 found	ParsCit 1.0