Solving the One-Class Problem Using Neighbourhood Measures
The problem of estimating high density regions from univariate or multivariate data samples is studied. To be more precise, we estimate minimum volume sets, whose probability is specified in advance. This problem arises in outlier detection and cluster analysis, and is strongly related to One-Class Support Vector Machines (SVM). In this paper we propose a new method to solve this problem, the Support Neighbour Machine (SNM). We show its properties and introduce a new class of kernels. Finally, numerical results illustrating the advantage of the new method are shown.
KeywordsSupport Vector Machine Outlier Detection Support Vector Machine Algorithm High Density Region True Mode
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