Optimized projections for sparse representation based classification

  • Authors:

  • Can-Yi Lu;De-Shuang Huang

  • Affiliations:

  • School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China and Department of Automation, University of Science and Technology of China, Hefei, Ch ...;School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

Dimensionality reduction (DR) methods have been commonly used as a principled way to understand the high-dimensional data such as facial images. In this paper, we propose a new supervised DR method called Optimized Projections for Sparse Representation based Classification (OP-SRC), which is based on the recent face recognition method, Sparse Representation based Classification (SRC). SRC seeks a sparse linear combination on all the training data for a given query image, and makes the decision by the minimal reconstruction residual. OP-SRC is designed on the decision rule of SRC, it aims to reduce the within-class reconstruction residual and simultaneously increase the between-class reconstruction residual on the training data. The projections are optimized and match well with the mechanism of SRC. Therefore, SRC performs well in the OP-SRC transformed space. The feasibility and effectiveness of the proposed method is verified on the Yale, ORL and UMIST databases with promising results.