Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Survey of Text Mining
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
The CMU Pose, Illumination, and Expression Database
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
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Journal of Cognitive Neuroscience
Introduction to Information Retrieval
Introduction to Information Retrieval
Subspace based feature selection for pattern recognition
Information Sciences: an International Journal
General Averaged Divergence Analysis
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Projections of Smooth Manifolds
Foundations of Computational Mathematics
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Sparsity preserving discriminant analysis for single training image face recognition
Pattern Recognition Letters
Robust Positive semidefinite L-Isomap Ensemble
Pattern Recognition Letters
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection
IEEE Transactions on Neural Networks
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
Sparse coding for image denoising using spike and slab prior
Neurocomputing
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Recently the underlying sparse representation structure in high dimensional data has attracted considerable interests in pattern recognition and computer vision. Sparse representation structure means the sparse reconstructive relationship of the data. In this paper, we propose a novel dimensionality reduction method called Sparse Regularization Discriminant Analysis (SRDA), which aims to preserve the sparse representation structure of the data when learning an efficient discriminating subspace. More specifically, SRDA first constructs a concatenated dictionary through class-wise PCA decompositions which conduct PCA on data from each class separately, and learns the sparse representation structure under the constructed dictionary quickly through matrix-vector multiplications. Then SRDA takes into account both the sparse representation structure and the discriminating efficiency by using the learned sparse representation structure as a regularization term of linear discriminant analysis. Finally, the optimal embedding of the data is learned via solving a generalized eigenvalue problem. The extensive and promising experimental results on four publicly available face data sets (Yale, Extended Yale B, ORL and CMU PIE) validate the feasibility and effectiveness of the proposed method.