Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised dimension reduction of intrinsically low-dimensional data
Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Incremental semi-supervised subspace learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Orthogonal locality preserving indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Statistical and computational analysis of locality preserving projection
ICML '05 Proceedings of the 22nd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Kernel class-wise locality preserving projection
Information Sciences: an International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction using constrained maximum variance mapping
Pattern Recognition
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Comparing and combining lighting insensitive approaches for face recognition
Computer Vision and Image Understanding
Face recognition using discriminant locality preserving projections
Image and Vision Computing
An adaptively weighted sub-pattern locality preserving projection for face recognition
Journal of Network and Computer Applications
Locality preserving and global discriminant projection with prior information
Machine Vision and Applications
LPP solution schemes for use with face recognition
Pattern Recognition
A deformation and lighting insensitive metric for face recognition based on dense correspondences
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Discriminative multi-manifold analysis for face recognition from a single training sample per person
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Geodesic Based Semi-supervised Multi-manifold Feature Extraction
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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In this paper, a manifold learning based method named local maximal margin discriminant embedding (LMMDE) is developed for feature extraction. The proposed algorithm LMMDE and other manifold learning based approaches have a point in common that the locality is preserved. Moreover, LMMDE takes consideration of intra-class compactness and inter-class separability of samples lying in each manifold. More concretely, for each data point, it pulls its neighboring data points with the same class label towards it as near as possible, while simultaneously pushing its neighboring data points with different class labels away from it as far as possible under the constraint of locality preserving. Compared to most of the up-to-date manifold learning based methods, this trick makes contribution to pattern classification from two aspects. On the one hand, the local structure in each manifold is still kept in the embedding space; one the other hand, the discriminant information in each manifold can be explored. Experimental results on the ORL, Yale and FERET face databases show the effectiveness of the proposed method.