In this paper, we address the problem of multi-label classification. We consider linear classifiers and propose to learn a prior over the space of labels to directly leverage the performance of such methods. This prior takes the form of a quadratic function of the labels and permits to encode both attractive and repulsive relations between labels. We cast this problem as a structured prediction one aiming at optimizing either the accuracies of the predictors or the F 1-score. This leads to an optimization problem closely related to the max-cut problem, which naturally leads to semidefinite and spectral relaxations. We show on standard datasets how such a general prior can improve the performances of multi-label techniques.
@article{arxiv.1506.01829,
title = {Semidefinite and Spectral Relaxations for Multi-Label Classification},
author = {Rémi Lajugie and Piotr Bojanowski and Sylvain Arlot and Francis Bach},
journal= {arXiv preprint arXiv:1506.01829},
year = {2015}
}