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  Estimating the support of a high-dimensional distribution.

Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., & Williamson, R. (2001). Estimating the support of a high-dimensional distribution. Neural computation, 13(7), 1443-1471. doi:10.1162/089976601750264965.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E2C6-C Version Permalink: http://hdl.handle.net/21.11116/0000-0005-A775-B
Genre: Journal Article

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Schölkopf, B1, Author              
Platt, JC, Author
Shawe-Taylor , J, Author
Smola, AJ1, Author              
Williamson, RC, Author
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1External Organizations, ou_persistent22              

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 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.

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 Dates: 2001-03
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1162/089976601750264965
BibTex Citekey: 970
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 13 (7) Sequence Number: - Start / End Page: 1443 - 1471 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591