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The Kernel Trick for Distances


Technical Report


A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as normbased distances in Hilbert spaces. It turns out that common kernel algorithms, such as SVMs and kernel PCA, are actually really distance based algorithms and can be run with that class of kernels, too. As well as providing a useful new insight into how these algorithms work, the present work can form the basis for conceiving new algorithms.

Author(s): Schölkopf, B.
Number (issue): MSR-TR-2000-51
Year: 2000
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Microsoft Research, Redmond, WA, USA

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {The Kernel Trick for Distances},
  author = {Sch{\"o}lkopf, B.},
  number = {MSR-TR-2000-51},
  organization = {Max-Planck-Gesellschaft},
  institution = {Microsoft Research, Redmond, WA, USA},
  school = {Biologische Kybernetik},
  year = {2000}