DeepAI AI Chat
Log In Sign Up

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

02/26/2017
by   Abraham Smith, et al.
Duke University
University of Wisconsin-Stout
GEOMETRIC DATA ANALYTICS
0

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.

READ FULL TEXT
04/19/2023

To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Deep neural networks have demonstrated remarkable performance in supervi...
07/07/2020

Information-theoretic convergence of extreme values to the Gumbel distribution

We show how convergence to the Gumbel distribution in an extreme value s...
08/05/2019

Elements of Generalized Tsallis Relative Entropy in Classical Information Theory

In this article, we propose a modification in generalised Tsallis entrop...
05/28/2016

Muffled Semi-Supervised Learning

We explore a novel approach to semi-supervised learning. This approach i...
02/01/2011

Information-theoretic measures associated with rough set approximations

Although some information-theoretic measures of uncertainty or granulari...
08/31/2018

The NEU Meta-Algorithm for Geometric Learning with Applications in Finance

We introduce a meta-algorithm, called non-Euclidean upgrading (NEU), whi...
11/04/2019

Improving Supervised Phase Identification Through the Theory of Information Losses

This paper considers the problem of Phase Identification in power distri...