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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Sampling for Subspace Face Recognition
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
Neighborhood MinMax projections
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A new algorithm, exemplar based Laplacian Discriminant Projection (ELDP), is proposed in this paper for supervised dimensionality reduction. ELDP aims at learning a linear transformation which is an extension of Linear Discriminant Analysis(LDA). Specifically, we define three scatter matrices using similarities based on representative exemplars which are found by affinity propagation clustering. After the transformation, the considered pairwise samples within the same exemplar subset and the same class are as close as possible, while those between classes are as far as possible. The structural information of classes is contained in the exemplar based Laplacian matrices. Thus the discriminant projection subspace can be derived by controlling the structural evolution of Laplacian matrices. The performance on several data sets demonstrates the competence of the proposed algorithm.