Efficient max-margin multi-label classification with applications to zero-shot learning

摘要

The goal in multi-label classification is to tag a data point with the subset of relevant labels from a pre-specified set. Given a set of L labels, a data point can be tagged with any of the 2L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations.