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Preprints, Working Papers, ... Year : 2015

Semidefinite and Spectral Relaxations for Multi-Label Classification


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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hal-01159321 , version 1 (03-06-2015)



Rémi Lajugie, Piotr Bojanowski, Sylvain Arlot, Francis Bach. Semidefinite and Spectral Relaxations for Multi-Label Classification. 2015. ⟨hal-01159321⟩
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