A Convex Relaxation for Weakly Supervised Classifiers

Understand

This paper introduces a general multi-class approach to weakly supervised classification.

• Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (EM), which may lead to local minima.
• To avoid this problem, we propose a cost function based on a convex relaxation of the soft-max loss.
• We then propose an algorithm specifically designed to efficiently solve the corresponding semidefinite program (SDP).