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
Fuzzy Clustering Models and Applications
Fuzzy Clustering Models and Applications
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
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
The Journal of Machine Learning Research
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant neighborhood embedding for classification
Pattern Recognition
Neighborhood discriminant projection for face recognition
Pattern Recognition Letters
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Multimodal biometrics using geometry preserving projections
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Metric learning by discriminant neighborhood embedding
Pattern Recognition
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Rank-One Projections With Adaptive Margins for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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For pattern analysis and recognition, it is necessary to find the meaningful low-dimensional representation of data in general. In the past decades, subspace learning methods have been regarded as the useful tools for feature extraction and dimensionality reduction. Without loss of generality, the linear subspace learning algorithms can be explained as the enhancement of the affinity and repulsion of several data pairs. Based on this point of view, a novel linear discriminant method, termed Marginal Discriminant Projections (MDP), is proposed to learn the marginal subspace. Rather than the existing marginal learning method, the maladjusted learning problem is alleviated by adopting a hierarchical fuzzy clustering approach, where the discriminative margin can be found adaptively and the iterative objective optimization is avoided. In addition, the proposed method is immune from the well-known curse of dimensionality problem, with respect to the presented subspace learning framework. Experiments on extensive datasets demonstrate the effectiveness of the proposed MDP for discriminative learning and recognition tasks.