A submodular-supermodular procedure with applications to discriminative structure learning

Understand

In this paper, we present an algorithm for minimizing the difference between two submodular functions using a variational framework which is based on (an extension of) the concave-convex procedure [17].

• Because several commonly used metrics in machine learning, like mutual information and conditional mutual information, are submodular, the problem of minimizing the difference of two submodular problems arises naturally in many machine learning applications.
• Two such applications are learning discriminatively structured graphical models and feature selection under computational complexity constraints.
• A commonly used metric for measuring discriminative capacity is the EAR measure which is the difference between two conditional mutual information terms.