PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-Supervised Learning via Generalized Maximum Entropy
Ayse Naz Erkan and Yasemin Altun
In: AISTATS 2010(2009).

Abstract

Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that maximizes an entropy function while respecting prior information represented as potential functions in miscellaneous forms of constraints and/or penalties. We extend this framework to semi-supervised learning by incorporating unlabeled data via modifications to these potential functions reflecting structural assumptions on the data geometry. The proposed approach leads to a family of discriminative semi-supervised algorithms, that are convex, scalable, inherently multi-class, easy to implement, and that can be kernelized naturally. Experimental evaluation of special cases shows the competitiveness of our methodology.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:6122
Deposited By:Yasemin Altun
Deposited On:08 March 2010