Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Neighborhood MinMax projections
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Robust Discriminant Analysis Based on Nonparametric Maximum Entropy
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Multi-subspace representation and discovery
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A closed form solution to robust subspace estimation and clustering
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Recovery of corrupted low-rank matrices via half-quadratic based nonconvex minimization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
l2, 1 Regularized correntropy for robust feature selection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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The low-rank representation (LRR) was presented recently and showed effective and robust for subspace segmentation. This paper presents a LRR-based discriminative projection method (LRR-DP) for robust feature extraction, by virtue of the underlying low-rank structure of data represesntation revealed by LRR. LRR-DP seeks a linear transformation such that in the transformed space, the between-class scatter (i.e. the distance between a sample and its between-class representation prototype) is as large as possible and simultaneously, the combination of the within-class scatter (i.e. the distance between a sample and its within-class representation prototype) and the scatter of noises is as small as possible. Our experiments were done using the Yale, Extended Yale B, AR face image databases and the PolyU palmprint database, and the results show that LRR-DP is always better than or comparable to other state-of-the-art methods.