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arXiv · 1702.07959

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

Abraham Smith·Paul Bendich·John Harer·Alex Pieloch·Jay Hineman
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Abstract

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.

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Abraham Smith, Paul Bendich, John Harer, Alex Pieloch, Jay Hineman. 2017-02-26. Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction. https://arxiv.org/abs/1702.07959

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