en fr Semi-supervised Learning with Regularized Laplacian LApprentissage Semi-supervise avec Laplacian Regularise Report as inadecuate




en fr Semi-supervised Learning with Regularized Laplacian LApprentissage Semi-supervise avec Laplacian Regularise - Download this document for free, or read online. Document in PDF available to download.

1 MAESTRO - Models for the performance analysis and the control of networks CRISAM - Inria Sophia Antipolis - Méditerranée 2 Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences 3 Faculty of Applied Mathematics and Control Processes

Abstract : We study a semi-supervised learning method based on the similarity graph and RegularizedLaplacian. We give convenient optimization formulation of the Regularized Laplacian method and establishits various properties. In particular, we show that the kernel of the methodcan be interpreted in terms of discrete and continuous time random walks and possesses several importantproperties of proximity measures. Both optimization and linear algebra methods can be used for efficientcomputation of the classification functions. We demonstrate on numerical examples that theRegularized Laplacian method is competitive with respect to the other state of the art semi-supervisedlearning methods.

Keywords : Wikipedia article classification Semi-supervised learning Graph-based learning Regularized Laplacian Proximity measure





Author: Konstantin Avrachenkov - Pavel Chebotarev - Alexey Mishenin -

Source: https://hal.archives-ouvertes.fr/



DOWNLOAD PDF




Related documents