Detecting Class-Independent Linear Relationships Within an Arbitrary Set of Features

Abstract

Classifiers for surveillance sonar systems are often designed to operate on large sets of predefined clues, or features. Sometimes the mathematical definitions for these features are poorly known. Other times the designer is not aware that a fixed and class-independent linear (or affine) relationship exists between subsets of features. We discuss a method based on Gram-Schmidt orthogonalization which allows the classifier designer to determine whether subsets of features have such relationships. Certain features can then be shown unnecessary by application of Wozencraft and Jacobs' "Theorem of Irrelevance". An approach is also described to rank features to aid in the selection of an effective subset.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA518947

Entities

People

  • Ashwin Sarma

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Data Science
  • Databases
  • Dimensionality Reduction
  • Electrical Engineering
  • Errors
  • Feature Extraction
  • Feature Selection
  • Machine Learning
  • Mathematics
  • Measurement
  • New York
  • Probability
  • Signal Processing
  • Standards

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Distributed Systems and Data Platform Development
  • Systems Analysis and Design