Fast Highdimensional Approximation with Sparse Occupancy Trees (Preprint)

Abstract

This paper is concerned with scattered data approximation in high dimensions. In this paper we presented algorithms for high dimensional approximation based on sparse occupancy trees which are well suited for large data sets and online learning. It was demonstrated that they offer a viable alternative to the k-nearest neighbor approximation. In particular the vertex algorithm seems to have further potential, since in some application it already outperforms classical approximation methods and there are several direction in which one can search for improvements, notably with regard to the construction of globally continuous approximants.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 04, 2010
Accession Number
ADA640828

Entities

People

  • Peter Binev
  • Philipp Lamby
  • Wolfgang Dahmen

Organizations

  • University of South Carolina

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Climate Change
  • Computer Programs
  • Data Mining
  • Data Sets
  • Differential Equations
  • Distance Learning
  • Equations
  • Information Science
  • Machine Learning
  • Military Research
  • Recovery
  • Standards
  • Statistics
  • Supervised Machine Learning
  • Trees (Data Structures)

Fields of Study

  • Computer science

Readers

  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.
  • Systems Analysis and Design