Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Algorithms in convex analysis to fit lp-distance matrices
Journal of Multivariate Analysis
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A comparative study of neural network based feature extraction paradigms
Pattern Recognition Letters
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 Kernel Approach to Metric Multidimensional Scaling
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A Nonlinear Feature Extraction Algorithm Using Distance Transformation
IEEE Transactions on Computers
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A Kernel Approach to Metric Multidimensional Scaling
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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The solution for the parameters of a nonlinear mapping in a metricm ultidimensional scaling by transformation, in which a stress criterion is optimised, satisfies a nonlinear eigenvector equation, which may be solved iteratively. This can be cast in a kernel-based framework in which the configuration of training samples in the transformation space may be found iteratively by successive linear projections, without the need for gradient calculations. A new data sample can be projected using knowledge of the kernel and the final configuration of data points.