Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Adaptive Quasiconformal Kernel Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast kernel-based nonlinear discriminant analysis for multi-class problems
Pattern Recognition
Rapid and Brief Communication: Fuzzy discriminant analysis with kernel methods
Pattern Recognition
Kernel clustering-based discriminant analysis
Pattern Recognition
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Optimally regularised kernel Fisher discriminant classification
Neural Networks
Kernel PCA for similarity invariant shape recognition
Neurocomputing
Kernel subspace LDA with optimized kernel parameters on face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Multimedia
A criterion for optimizing kernel parameters in KBDA for image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Directional discriminant analysis based on nearest feature line
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Future Generation Computer Systems
Information Sciences: an International Journal
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
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Kernel discriminant analysis (KDA) is effective to extract nonlinear discriminative features of input samples using the kernel trick. However, the conventional KDA algorithm endures the kernel selection which has significant impact on the performances of KDA. In order to overcome this limitation, a novel nonlinear feature extraction method called adaptive quasiconformal kernel discriminant analysis (AQKDA) is proposed in this paper. AQKDA maps the data from the original input space to the high dimensional kernel space using a quasiconformal kernel. The adaptive parameters of the quasiconformal kernel are automatically calculated through optimizing an objective function designed for measuring the class separability of data in the feature space. Consequently, the nonlinear features extracted by AQKDA have the larger class separability compared with KDA. Experimental results on the two real-world datasets demonstrate the effectiveness of the proposed method.