  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 Projection 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 make 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.
