3 years ago

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

Jay Hineman, Alex Pieloch, John Harer, Abraham Smith, Paul Bendich

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.

Publisher URL: http://arxiv.org/abs/1702.07959

DOI: arXiv:1702.07959v3

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