Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction

02/26/2017

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by   Abraham Smith, et al.

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Duke University

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University of Wisconsin-Stout

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GEOMETRIC DATA ANALYTICS

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We introduce a new algorithm, called CDER, for supervised machine learning that merges the multi-scale geometric properties of Cover Trees with the information-theoretic properties of entropy. CDER applies to a training set of labeled pointclouds embedded in a common Euclidean space. If typical pointclouds corresponding to distinct labels tend to differ at any scale in any sub-region, CDER can identify these differences in (typically) linear time, creating a set of distributional coordinates which act as a feature extraction mechanism for supervised learning. We describe theoretical properties and implementation details of CDER, and illustrate its benefits on several synthetic examples.