Year: 2017
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Smith, Abraham, Bendich, Paul, Harer, John, Pieloch, Alex, Hineman, Jay
Institutions: University of Wisconsin–Stout, Duke University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2515541506, G5270150132
OpenAlex ID: W2594079634