Semi-Supervized Penalized Output Kernel Regression for Link Prediction
Céline Bouard, Florence d'Alché-Buc and Marie Szafranski
In: ICML 2011, 28 Jun - 2 Jul 2011, Bellevue, Washingtown, USA..

Abstract

Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vector-valued functions, we establish a new representer theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and show its relevance using numerical experiments on artificial networks and two real applications using a very low percentage of labeled data in a transductive setting.

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