Journal
MACHINE LEARNING
Volume 46, Issue 1-3, Pages 11-19Publisher
KLUWER ACADEMIC PUBL
DOI: 10.1023/A:1012485807823
Keywords
metric multidimensional scaling; MDS; kernel PCA; eigenproblem
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In this note we show that the kernel PCA algorithm of Scholkopf, Smola, and Muller (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 parallel tox - y parallel to. 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.
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