Localized Exploratory Projection Pursuit,

Abstract

Based on CART, we introduce a recursive partitioning method for high dimensional space which partitions the data using low dimensional features. The low dimensional features are extracted via an exploratory projection pursuit (EPP) method, localized to each node in the tree. In addition, we present an exploratory splitting rule that is potentially less biased to the training data. This leads to a nonparametric classifier for high dimensional space that has local feature extractors optimized to different regions in the input space.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007141

Entities

People

  • Nathan Intrator

Organizations

  • Brown University

Tags

DTIC Thesaurus Topics

  • Applied Computer Science
  • Artificial Intelligence
  • Automata Theory
  • Computer Science
  • Data Science
  • Engineering
  • Information Science
  • Machine Learning
  • Network Science
  • Splitting
  • Statistics
  • Theoretical Computer Science
  • Training

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • Space