Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A new optimization criterion for generalized discriminant analysis on undersampled problems
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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In this paper, we propose a nonlinear feature extractionmethod which is based on centroids and kernel functions.The dimension reducing nonlinear transformation isobtained by implicitly mapping the input data into a featurespace using a kernel function, and then finding a linearmapping based on an orthonormal basis of centroids in thefeature space that maximally separates the between-classrelationship. The proposed method utilizes an efficient algorithmto compute an orthonormal basis of centroids in thefeature space transformed by a kernel function and achievesdramatic computational savings. The experimental resultsdemonstrate that our method is capable of extracting non-linearfeatures effectively so that competitive performanceof classification can be obtained in the reduced dimensionalspace.