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arXiv · 1506.01829

Semidefinite and Spectral Relaxations for Multi-Label Classification

Rémi Lajugie·Piotr Bojanowski·Sylvain Arlot·Francis Bach
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Abstract

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.

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Rémi Lajugie, Piotr Bojanowski, Sylvain Arlot, Francis Bach. 2015-06-05. Semidefinite and Spectral Relaxations for Multi-Label Classification. https://arxiv.org/abs/1506.01829

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