PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semigroup kernels on measures
Marco Cuturi, Kenji Fukumizu and Jean-Philippe Vert
Journal of Machine Learning Research Volume 6, pp. 1169-1198, 2005.

Abstract

We present a family of positive definite kernels on measures, characterized by the fact that the value of the kernel between two measures is a function of their sum. These kernels can be used to derive kernels on structured objects, such as images and texts, by representing these objects as sets of components, such as pixels or words, or more generally as measures on the space of components. Several kernels studied in this work make use of common quantities defined on measures such as entropy or generalized variance to detect similarities. Given an a priori kernel on the space of components itself, the approach is further extended by restating the previous results in a more efficient and flexible framework using the "kernel trick". Finally, a constructive approach to such positive definite kernels through an integral representation theorem is proved, before presenting experimental results on a benchmark experiment of handwritten digits classification to illustrate the validity of the approach.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1402
Deposited By:Jean-Philippe Vert
Deposited On:28 November 2005