AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
DensityRank: a novel feature ranking method based on kernel estimation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Mining physiological conditions from heart rate variability analysis
IEEE Computational Intelligence Magazine
Feature selection with redundancy-constrained class separability
IEEE Transactions on Neural Networks
Automatic classification of lymphoma images with transform-based global features
IEEE Transactions on Information Technology in Biomedicine
Hippocampal shape classification using redundancy constrained feature selection
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation
Computer Vision and Image Understanding
Support Vector Machine incorporated with feature discrimination
Expert Systems with Applications: An International Journal
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Weighted principal component extraction with genetic algorithms
Applied Soft Computing
Evaluation of feature selection by multiclass kernel discriminant analysis
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Incorporation of radius-info can be simple with SimpleMKL
Neurocomputing
Modeling spectral data based on mutual information and kernel extreme learning machines
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Automatic correction of annotation boundaries in activity datasets by class separation maximization
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Classification can often benefit from efficient feature selection. However, the presence of linearly nonseparable data, quick response requirement, small sample problem and noisy features makes the feature selection quite challenging. In this work, a class separability criterion is developed in a high-dimensional kernel space, and feature selection is performed by the maximization of this criterion. To make this feature selection approach work, the issues of automatic kernel parameter tuning, the numerical stability, and the regularization for multi-parameter optimization are addressed. Theoretical analysis uncovers the relationship of this criterion to the radius-margin bound of the SVMs, the KFDA, and the kernel alignment criterion, providing more insight on using this criterion for feature selection. This criterion is applied to a variety of selection modes with different search strategies. Extensive experimental study demonstrates its efficiency in delivering fast and robust feature selection.