Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The Amsterdam Library of Object Images
International Journal of Computer Vision
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
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Effective Feature Extraction in High-Dimensional Space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Large Margin Subspace Learning for feature selection
Pattern Recognition
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Many applications in machine learning and computer vision come down to feature representation and reduction. Manifold learning seeks the intrinsic low-dimensional manifold structure hidden in the high-dimensional data. In the past few years, many local discriminant analysis methods have been proposed to exploit the discriminative submanifold structure by extending the manifold learning idea to supervised ones. Particularly, marginal Fisher analysis (MFA) finds the local interclass margin for feature extraction and classification. However, since the limited data pairs are employed to determine the discriminative margin, such method usually suffers from the maladjusted learning as we introduced in this paper. To improve the discriminant ability of MFA, we incorporate the marginal Fisher idea with the global between-class separability criterion (BCSC), and propose a novel supervised learning method, called local and global margin projections (LGMP), where the maladjusted learning problem can be alleviated. Experimental evaluation shows that the proposed LGMP outperforms the original MFA.