Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Graph-optimized locality preserving projections
Pattern Recognition
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
LPP solution schemes for use with face recognition
Pattern Recognition
Gabor feature based face recognition using supervised locality preserving projection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
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In this paper, we address the graph-based linear manifold learning method for object recognition. The proposed method is called enhanced Locality Preserving Projections. The main contribution is a parameterless computation of the affinity matrix that draws on the notion of meaningful and adaptive neighbors. It integrates two interesting properties: (i) being entirely parameter-free and (ii) the mapped data are uncorrelated. The proposed method has been integrated in the framework of three graph-based embedding techniques: Locality Preserving Projections (LPP), Orthogonal Locality Preserving Projections (OLPP), and supervised LPP (SLPP). Recognition tasks on six public face databases show an improvement over the results of LPP, OLPP, and SLPP. The proposed approach could also be applied to other category of objects.