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Title:On a Connection between Kernel PCA and Metric Multidimensional Scaling
Authors: Chris Williams
Date: 2002
Publication Title:Machine Learning
Publication Type:Journal Article Publication Status:Published
Volume No:46 Page Nos:11-19
In this note we show that the kernel PCA algorithm of Schlkopf, Smola, and Mller (Neural Computation, 10, 12991319.) 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.
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Bibtex format
author = { Chris Williams },
title = {On a Connection between Kernel PCA and Metric Multidimensional Scaling},
journal = {Machine Learning},
publisher = {Springer},
year = 2002,
volume = {46},
pages = {11-19},
doi = {10.1023/A:1012485807823},
url = {,2,19;journal,30,139;linkingpublicationresults,1:100309,1},

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