Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
A modular eigen subspace scheme for high-dimensional data classification
Future Generation Computer Systems - Special issue: Geocomputation
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Ensemble-Based discriminant manifold learning for face recognition
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Editorial: High performance biometrics recognition algorithms and systems
Future Generation Computer Systems
Face recognition with enhanced local directional patterns
Neurocomputing
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This paper presents a novel supervised linear dimensionality reduction approach called maximum margin neighborhood preserving embedding (MMNPE). The central idea is to modify the neighborhood preserving embedding by maximizing the maximum margin distance while preserving the geometric structure of the manifold. Experimental results conducted on the ORL database, the Yale database and the VALID face database indicate the effectiveness of the proposed MMNPE.