ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel projection classifiers with suppressing features of other classes
Neural Computation
A comparative analysis of kernel subspace target detectors for hyperspectral imagery
EURASIP Journal on Applied Signal Processing
Person recognition using facial video information: A state of the art
Journal of Visual Languages and Computing
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Learning kernel subspace classifier
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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The kernel based nonlinear subspace (KNS) method is proposed for multi-class pattern classification. This method consists of the nonlinear transformation of feature spaces defined by kernel functions and subspace method in transformed high-dimensional spaces. The support vector machine, a nonlinear classifier based on a kernel function technique, shows excellent classification performance, however, its computational cost increases exponentially with the number of patterns and classes. The linear subspace method is a technique for multi-category classification, but it fails when the pattern distribution has nonlinear characteristics or the feature space dimension is low compared to the number of classes. The proposed method combines the advantages of both techniques and realizes multi-class nonlinear classifiers with better performance in less computational time. We show that a nonlinear subspace method can be formulated by nonlinear transformations defined through kernel functions and that its performance is better than that obtained by conventional methods.