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SSG Seminar Abstract

Geometric entropy minimization (GEM)

Alfred Hero
University of Michigan, Ann Arbor

We introduce geometric entropy minimization as a framework for non-parametric inference. The framework is based on the asymptotic behavior of k-point minimal graphs that tends to pick out most concentrated regions of a data sample. In this framework we formulate a transductive anomaly detection method that is asymptotically equivalent to minimum volume set estimation but is applicable to high dimensional feature spaces. We also use this framework to derive a spectral clustering method, called dual rooted diffusions, and apply it to semi-supervised classification problems.

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