Combined Compression and Classification with Learning Vector Quantization

Abstract

Combined compression and classification problems are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from aided target recognition (ATR), to medical diagnosis, to speech recognition, to fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector quantization (LVQ) based algorithm for the combined compression and classification problem. We show convergence of the algorithm using techniques from stochastic approximation, namely, the ODE method. We illustrate the performance of our algorithm with some examples.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1998
Accession Number
ADA438572

Entities

People

  • John Baras
  • Subhrakanti Dey

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Compression
  • Data Sets
  • Distribution Functions
  • Electrical Engineering
  • Engineering
  • Gaussian Distributions
  • Learning
  • Normal Distribution
  • Probability
  • Probability Distributions
  • Random Variables
  • Recognition
  • Simulations
  • Target Recognition
  • Two Dimensional

Fields of Study

  • Computer science
  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Sensor Fusion and Tracking Systems.
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms