Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Generalized Low Rank Approximations of Matrices
Machine Learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting a graph to vector data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Ubiquitously supervised subspace learning
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Face recognition using fisher non-negative matrix factorization with sparseness constraints
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Trace quotient problems revisited
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Comments on “Efficient and Robust Feature Extraction by Maximum Margin Criterion”
IEEE Transactions on Neural Networks
Contextual constraints based linear discriminant analysis
Pattern Recognition Letters
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Image security and biometrics: a review
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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A new subspace learning algorithm called locality preserving discriminant projections (LPDP) is proposed by adding the criterion of maximum margin criterion (MMC) into the objective function of locality preserving projections (LPP). LPDP retains the locality preserving characteristic of LPP and utilizes the global discriminative structures obtained from MMC, which can maximize the between-class distance and minimize the within-class distance. Thus, our proposed LPDP combining manifold criterion and Fisher criterion has more discriminanting power, and is more suitable for recognition tasks than LPP, which considers only the local information for classification tasks. Moreover, two kinds of tensorized (multilinear) forms of LPDP are also derived in this paper. One is iterative while the other is non-iterative. The proposed LPDP method is applied to face and palmprint biometrics and is examined using the Yale and ORL face image databases, as well as the PolyU palmprint database. Experimental results demonstrate the effectiveness of the proposed LPDP method.