Regularization theory and neural networks architectures
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
Mustererkennung 1998, 20. DAGM-Symposium
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
KPCA denoising and the pre-image problem revisited
Digital Signal Processing
Multi-layered hand and face tracking for real-time gesture recognition
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A Closed-form Solution for the Pre-image Problem in Kernel-based Machines
Journal of Signal Processing Systems
Kernel uncorrelated discriminant analysis for radar target recognition
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Sparse kernel fisher discriminant analysis
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
The pre-image problem in kernel methods
IEEE Transactions on Neural Networks
Efficient semantic kernel-based text classification using matching pursuit KFDA
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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Kernel Fisher Discriminant Analysis (KFDA) improves greatly the classification accuracy of FDA via using kernel trick. However, the final solution of KFDA is expressed as an expansion of all training examples, which seriously undermines the classification efficiency, especially in real-time applications. This paper proposes a novel framework to construct sparse KFDA using pre-image reconstruction. The proposed method (PR-KFDA) appends greedily the pre-image of the residual between the current approximate model and the original KFDA model in feature space with the local optimal Fisher coefficients to acquire sparse representation of KFDA solution. Experimental results show that PR-KFDA can reduce the solution of KFDA effectively while maintaining comparable test accuracy.