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Optimized projections for sparse representation based classification
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DOI：10.1016/j.neucom.2013.01.009.png)
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Abstract
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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. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.
Keywords:
Dimensionality reduction
Sparse representation
Face recognition
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6.5
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2.5W
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6.5W
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