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ICML
2002
IEEE

Diffusion Kernels on Graphs and Other Discrete Input Spaces

15 years 7 days ago
Diffusion Kernels on Graphs and Other Discrete Input Spaces
The application of kernel-based learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation idea. In particular, we focus on generating kernels on graphs, for which we propose a special class of exponential kernels called diffusion kernels, which are based on the heat equation and can be regarded as the discretization of the familiar Gaussian kernel of Euclidean space.
Risi Imre Kondor, John D. Lafferty
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2002
Where ICML
Authors Risi Imre Kondor, John D. Lafferty
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