Accession Number:

AD1022332

Title:

Sparse Representation Based Classification with Structure Preserving Dimension Reduction

Descriptive Note:

Journal Article

Corporate Author:

University of Rhode Island Kingston United States

Report Date:

2014-03-13

Pagination or Media Count:

17.0

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.

Subject Categories:

  • Cybernetics
  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE