Feature Extraction Using Laplacian Maximum Margin Criterion
Neural Processing Letters
Dictionary learning based on Laplacian score in sparse coding
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Face Recognition Using Kernel UDP
Neural Processing Letters
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Sparse embedding: a framework for sparsity promoting dimensionality reduction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
An adaptive regularization method for sparse representation
Integrated Computer-Aided Engineering
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Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the dimensionality reduction (DR) of face images under the framework of SRC. Although one important merit of SRC is that it is insensitive to DR or feature extraction, a well trained projection matrix can lead to higher FR rate at a lower dimensionality. An SRC oriented unsupervised DR algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed DR algorithm over PCA or random projection based DR under the SRC framework.