Intelligent Data Fusion Using Sparse Representations and Nonlinear Dimensionality Reduction

Abstract

We propose a new method for performing data fusion and subsequent classification in an information-efficient manner. We argue that an algorithm that can find sparse, low-dimensional representations of data is an excellent candidate for data fusion and classification. Two recent developments in signal processing are investigated: 1) The use of over-determined dictionaries (e.g., frames), and 2) the use of so-called nonlinear dimensionality reduction techniques.

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Document Details

Document Type
Technical Report
Publication Date
Sep 28, 2009
Accession Number
ADA507109

Entities

People

  • Frank Bucholtz
  • Jonathan M. Nichols
  • Joseph V. Michalowicz

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Advanced Electronics
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Coefficients
  • Compressed Sensing
  • Data Analysis
  • Data Compression
  • Data Fusion
  • Data Modeling
  • Data Reduction
  • Detection
  • Dimensionality Reduction
  • Geometry
  • Information Science
  • Military Research
  • Signal Processing
  • Supervised Machine Learning
  • Target Detection

Readers

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