The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Kernel Approach to Metric Multidimensional Scaling
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Pattern Recognition
Neural Computation
Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding
International Journal of Computer Vision
Locality preserving discriminant projections
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Euclidean distances, soft and spectral clustering on weighted graphs
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Kernel principal component analysis for stochastic input model generation
Journal of Computational Physics
MusicGalaxy: a multi-focus zoomable interface for multi-facet exploration of music collections
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Facial image reconstruction by SVDD-Based pattern de-noising
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Kernel principal components are maximum entropy projections
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Simplified support vector machines via kernel-based clustering
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Correlated attribute transfer with multi-task graph-guided fusion
Proceedings of the 20th ACM international conference on Multimedia
Optimal semi-supervised metric learning for image retrieval
Proceedings of the 20th ACM international conference on Multimedia
Regional and Entropy component analysis based remote sensing images fusion
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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.