Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Matrix computations (3rd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The Journal of Machine Learning Research
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A feature extraction approach based on typical samples and its application to face recognition
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Improving multiclass pattern recognition with a co-evolutionary RBFNN
Pattern Recognition Letters
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
A New Solution Scheme of Unsupervised Locality Preserving Projection Method for the SSS Problem
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Sparse multinomial kernel discriminant analysis (sMKDA)
Pattern Recognition
Determine the Kernel Parameter of KFDA Using a Minimum Search Algorithm
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
A feature extraction approach based on typical samples and its application to face recognition
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
LPP solution schemes for use with face recognition
Pattern Recognition
A fast method of feature extraction for kernel MSE
Neurocomputing
An automatic index validity for clustering
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Pruning least objective contribution in KMSE
Neurocomputing
Discriminant analysis based on nearest feature line
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting
Neural Processing Letters
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Nonlinear discriminant analysis may be transformed into the form of kernel-based discriminant analysis. Thus, the corresponding discriminant direction can be solved by linear equations. From the view of feature space, the nonlinear discriminant analysis is still a linear method, and it is provable that in feature space the method is equivalent to Fisher discriminant analysis. We consider that one linear combination of parts of training samples, called ''significant nodes'', can replace the total training samples to express the corresponding discriminant vector in feature space to some extent. In this paper, an efficient algorithm is proposed to determine ''significant nodes'' one by one. The principle of determining ''significant nodes'' is simple and reasonable, and the consequent algorithm can be carried out with acceptable computation cost. Depending on the kernel functions between test samples and all ''significant nodes'', classification can be implemented. The proposed method is called fast kernel-based nonlinear method (FKNM). It is noticeable that the number of ''significant nodes'' may be much smaller than that of the total training samples. As a result, for two-class classification problems, the FKNM will be much more efficient than the naive kernel-based nonlinear method (NKNM). The FKNM can be also applied to multi-class via two approaches: one-against-the-rest and one-against-one. Although there is a view that one-against-one is superior to one-against-the-rest in classification efficiency, it seems that for the FKNM one-against-the-rest is more efficient than one-against-one. Experiments on benchmark and real datasets illustrate that, for two-class and multi-class classifications, the FKNM is effective, feasible and much efficient.