Structural, Syntactic, and Statistical Pattern Recognition

Volume 2396 of the series Lecture Notes in Computer Science pp 452-460


A Kernel Approach to Metric Multidimensional Scaling

  • Andrew WebbAffiliated withQinetiQ

* Final gross prices may vary according to local VAT.

Get Access


The solution for the parameters of a nonlinear mapping in a metric multidimensional 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.


multidimensional scaling kernel representation nonlinear feature extraction