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Estimating the support of a high-dimensional distribution. (English) Zbl 1009.62029
Summary: 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.

MSC:
62G07 Density estimation
90C90 Applications of mathematical programming
Software:
SVMlight
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References:
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