PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Supervised graph inference
Jean-Philippe Vert and Yoshihiro Yamanishi
In: Advances in Neural Information Processing Systems (2005) MIT Press , Cambridge, MA , pp. 1433-1440.

Abstract

We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learning of a mapping of the vertices to a Euclidean space where the graph is easy to infer, and can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of metabolic network reconstruction from genomic data.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1405
Deposited By:Jean-Philippe Vert
Deposited On:28 November 2005