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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.