Abstract
In this note we show that the kernel PCA algorithm of Schölkopf, Smola, and Müller (Neural Computation, 10, 1299–1319.) can be interpreted as a form of metric multidimensional scaling (MDS) when the kernel function k(x, y) is isotropic, i.e. it depends only on ‖x − y‖. This leads to a metric MDS algorithm where the desired configuration of points is found via the solution of an eigenproblem rather than through the iterative optimization of the stress objective function. The question of kernel choice is also discussed.
Original language | English |
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Pages (from-to) | 11-19 |
Number of pages | 9 |
Journal | Machine Learning |
Volume | 46 |
Issue number | 1-3 |
DOIs | |
Publication status | Published - Jan 2002 |
Keywords / Materials (for Non-textual outputs)
- metric multidimensional scaling
- MDS
- kernel PCA
- eigenproblem