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
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database
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
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stabilizing Classifiers for Very Small Sample Sizes
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Discriminant Subspace Analysis: A Fukunaga-Koontz Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Recently, Graph Embedding Framework has been proposed for feature extraction. Although many algorithms can be used to extract the discriminant transformation of supervised graph embedding, it is still an open issue which algorithm is more robust. In this paper, we first review the classical algorithms which can extract the discriminant transformation of linear discriminant analysis (LDA), and then generalize these classical algorithms for computing the discriminant transformation of supervised graph embedding. Secondly, we theoretically analyze the robustness of these generalized algorithms. Finally, in order to overcome the disadvantages of these generalized algorithms, we propose an effective method, called total space solution for supervised graph embedding (TSS/SGE), to extract the robust discriminant transformation of Supervised Graph Embedding. Extensive experiments and comprehensive comparison on real-world data are performed to demonstrate the robustness of our proposed TSS/SGE.