Semidefinite and Spectral Relaxations for Multi-Label Classification

TL;DR

This paper introduces a novel approach to multi-label classification by learning a quadratic prior over labels, enabling the encoding of label relations and improving performance through semidefinite and spectral relaxations.

Contribution

It proposes a new structured prediction framework with a quadratic prior for multi-label classification, leveraging relaxations related to the max-cut problem.

Findings

Improved multi-label classification accuracy on standard datasets.

Effective encoding of label relations through quadratic priors.

Demonstrated benefits of semidefinite and spectral relaxations.

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.