A convex relaxation for weakly supervised classifiers

Armand Joulin (1, 2) , Francis Bach (2, 3)

1 WILLOW - Models of visual object recognition and scene understanding
2 SIERRA - Statistical Machine Learning and Parsimony
3 LIENS - Laboratoire d'informatique de l'école normale supérieure

This paper introduces a general multi-class approach to weakly supervised classification. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (EM), which may lead to local minima. To avoid this problem, we propose a cost function based on a convex relaxation of the soft-max loss. We then propose an algorithm specifically designed to efficiently solve the corresponding semidefinite program (SDP). Empirically, our method compares favorably to standard ones on different datasets for multiple instance learning and semi-supervised learning, as well as on clustering tasks.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Armand Joulin, Francis Bach. A convex relaxation for weakly supervised classifiers. ICML 2012 International Conference on Machine Learning, Jun 2012, Edinburgh, United Kingdom. pp.641. ⟨hal-00717450⟩