Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Probing the Pareto Frontier for Basis Pursuit Solutions
SIAM Journal on Scientific Computing
Sparse Representation Classifier Steered Discriminative Projection
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Face recognition via Weighted Sparse Representation
Journal of Visual Communication and Image Representation
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Dimensionality reduction(DR) methods have commonly been used as a principled way to understand the high-dimensional data such as face images. In this paper, we propose a new supervised DR method called Optimized Projection for Sparse Representation based Classification(OP-SRC). SRC assumes that any new sample will approximately lie in the linear span of the training samples sharing the same class label. The decision of SRC is based on the reconstruction residual. OP-SRC aims to reduce the within-class reconstruction residual and simultaneously increases the between-class reconstruction residual. Therefore, SRC performs well in the OP-SRC transformed space. The feasibility and effectiveness of the proposed method is verified on Yale and ORL with promising results.