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Semi-supervised Penalized Output Kernel Regression for Link Prediction

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 repre- senter 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 us- ing numerical experiments on arti cial net- works and two real applications using a very low percentage of labeled data in a transduc- tive setting.
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Submitted on : Thursday, November 28, 2013 - 5:20:33 PM
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  • HAL Id : hal-00654123, version 1
  • PRODINRA : 476223



Celine Brouard, Florence d'Alché-Buc, Marie Szafranski. Semi-supervised Penalized Output Kernel Regression for Link Prediction. 28th International Conference on Machine Learning (ICML 2011), Jun 2011, Bellevue, WA, United States. pp.593--600. ⟨hal-00654123⟩



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