Estimating the Support of a HighDimensional Distribution
Schölkopf, B., Platt, J.C., ShaweTaylor, J.S., Smola, A.J. and Williamson, R.C. (2001) Estimating the Support of a HighDimensional Distribution Neural Computation, 13, (7), pp. 14431471.
Download
Full text not available from this repository.
Description/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.
Item Type:  Article  

Organisations:  Electronics & Computer Science  
ePrint ID:  259789  
Date : 


Date Deposited:  17 Aug 2004  
Last Modified:  17 Apr 2017 22:24  
Further Information:  Google Scholar  
URI:  http://eprints.soton.ac.uk/id/eprint/259789 
Actions (login required)
View Item 