Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust linear dimensionality reduction
IEEE Transactions on Visualization and Computer Graphics
Automatic face authentication with self compensation
Image and Vision Computing
Palmprint recognition with improved two-dimensional locality preserving projections
Image and Vision Computing
Semi-random subspace method for face recognition
Image and Vision Computing
Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Two-dimensional discriminant locality preserving projections for face recognition
Pattern Recognition Letters
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
Short Communication: A novel local preserving projection scheme for use with face recognition
Expert Systems with Applications: An International Journal
Regularized locality preserving projections and its extensions for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Orthogonal locally discriminant projection for palmprint recognition
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Face recognition via two dimensional locality preserving projection in frequency domain
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Feature Extraction Using Laplacian Maximum Margin Criterion
Neural Processing Letters
Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition
Neural Processing Letters
The analysis of parameters t and k of LPP on several famous face databases
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Robust regression for face recognition
Pattern Recognition
Supervised optimal locality preserving projection
Pattern Recognition
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Supervised Discriminant Projection with Its Application to Face Recognition
Neural Processing Letters
Nearest-neighbor classifier motivated marginal discriminant projections for face recognition
Frontiers of Computer Science in China
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Kernel fisher LPP for face recognition
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Global plus local: A complete framework for feature extraction and recognition
Pattern Recognition
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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Locality Preserving Projections (LPP) is a linear projective map that arises by solving a variational problem that optimally preserves the neighborhood structure of the data set. Though LPP has been applied in many domains, it has limits to solve recognition problem. Thus, Discriminant Locality Preserving Projections (DLPP) is presented in this paper. The improvement of DLPP algorithm over LPP method benefits mostly from two aspects: One aspect is that DLPP tries to find the subspace that best discriminates different face classes by maximizing the between-class distance, while minimizing the within-class distance; The other aspect is that DLPP reduces the energy of noise and transformation difference as much as possible without sacrificing much of intrinsic difference. In the experiments, DLPP achieves better face recognition performance than LPP.