Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning optimization in simplifying fuzzy rules
Fuzzy Sets and Systems
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
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Kernel class-wise locality preserving projection
Information Sciences: an International Journal
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
Kernel optimization-based discriminant analysis for face recognition
Neural Computing and Applications
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
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
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
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
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
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Evolving fuzzy pattern trees for binary classification on data streams
Information Sciences: an International Journal
A Hopfield neural network applied to the fuzzy maximum cut problem under credibility measure
Information Sciences: an International Journal
Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home
Information Sciences: an International Journal
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Kernel learning is becoming an important research topic in the area of machine learning, and it has wide applications in pattern recognition, computer vision, image and signal processing. Kernel learning provides a promising solution to nonlinear problems, including nonlinear feature extraction, classification and clustering. However, in kernel-based systems, the problem of the kernel function and its parameters remains to be solved. Methods of choosing parameters from a discrete set of values have been presented in previous studies, but these methods do not change the data distribution structure in the kernel-based mapping space. Accordingly, performance is not improved because the current kernel optimization does not change the data distribution. Based on this problem, this paper presents a uniform framework for kernel self-optimization with the ability to adjust the data structure. The data-dependent kernel is extended and applied to kernel learning, and optimization equations with two criteria for measuring data discrimination are used to solve the optimal parameter values. Some experiments are performed to evaluate the performance in popular kernel learning methods, including kernel principal components analysis (KPCA), kernel discriminant analysis (KDA) and kernel locality-preserving projection (KLPP). These evaluations show that the framework of kernel self-optimization is feasible for enhancing kernel-based learning methods.