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In this paper, we investigate the problem of estimating high-density regions from univariate or multivariate data samples. We estimate minimum volume sets, whose probability is specified in advance, known in the literature as density contour clusters. This problem is strongly related to One-Class Support Vector Machines (OCSVM). We propose a new method to solve this problem, the One-Class Neighbor Machine (OCNM) and we show its properties. In particular, the OCNM solution asymptotically converges to the exact minimum volume set prespecified. Finally, numerical results illustrating the advantage of the new method are shown.
Index Terms- Density estimation, kernel methods, One-Class Support Vector Machines.

A. Mu? and J. M. Moguerza, "Estimation of High-Density Regions Using One-Class Neighbor Machines," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 476-480, 2006.
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