Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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In spite of the popularity of Fisher discriminant analysis in the realm of feature extraction and pattern classification, it is beyond the capability of Fisher discriminant analysis to extract nonlinear structures from the data. That is where the kernel Fisher discriminant algorithm sets in the scenario of supervised learning. In this article, a new trail is blazed in developing innovative and effective algorithm for polychotomous kernel Fisher discriminant with the capability in estimating the posterior probabilities, which is exceedingly necessary and significant in solving complex nonlinear pattern recognition problems arising from the real world. Different from the conventional 'divide-and-combine' approaches to polychotomous classification problems, such as pairwise and one-versus-others, the method proposed herein synthesizes the multi-category classifier via the induction of top-to-down binary tree by means of kernelized group clustering algorithm. The deficiencies inherited in the conventional multi-category kernel Fisher discriminant are surmounted and the simulation on a benchmark image dataset demonstrates the superiority of the proposed approach.