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Approximating Hierarchical MV-sets for Hierarchical Clustering
The goal of hierarchical clustering is to construct a cluster tree, which can be viewed as the modal structure of a density.
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions
In this paper we introduce a novel method that can efficiently estimate a family of hierarchical dense sets in high-dimensional distributions.
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
We propose an efficient, generalized, nonparametric, statistical Kolmogorov-Smirnov test for detecting distributional change in high-dimensional data.
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