Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Engineering Applications of Artificial Intelligence
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Recently, sparsity preserving projections (SPP) algorithm has been proposed, which combines l"1-graph preserving the sparse reconstructive relationship of the data with the classical dimensionality reduction algorithm. However, when applied to classification problem, SPP only focuses on the sparse structure but ignores the label information of samples. To enhance the classification performance, a new algorithm termed discriminative learning by sparse representation projections or DLSP for short is proposed in this paper. DLSP algorithm incorporates the merits of both local interclass geometrical structure and sparsity property. That makes it possess the advantages of the sparse reconstruction, and more importantly, it has better capacity of discrimination, especially when the size of the training set is small. Extensive experimental results on serval publicly available data sets show the feasibility and effectiveness of the proposed algorithm.