Sparse Representation Based Classification with Structure Preserving Dimension Reduction

Abstract

Sparse-representation-based classification (SRC), which classifies data based on the sparse reconstruction error, has been a new technique in pattern recognition. However, the computation cost for sparse coding is heavy in real applications. In this paper, various dimension reduction methods are studied in the context of SRC to improve classification accuracy as well as reduce computational cost. A feature extraction method, i.e., principal component analysis, and feature selection methods, i.e., Laplacian score and Pearson correlation coefficient, are applied to the data preparation step to preserve the structure of data in the lower-dimensional space.

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Document Details

Document Type
Technical Report
Publication Date
Mar 13, 2014
Accession Number
AD1022332

Entities

People

  • Guang Yang
  • Haibo He
  • Hong Man
  • Jin Xu
  • Yafeng Yin

Organizations

  • University of Rhode Island

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Cognitive Science
  • Computer Programming
  • Computers
  • Data Mining
  • Dimensionality Reduction
  • Feature Extraction
  • Feature Selection
  • Information Processing
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Linear Algebra
  • Naval Architecture and Marine Engineering.

Technology Areas

  • AI & ML
  • Space