Large-Margin Metric Learning for Constrained Partitioning Problems

Rémi Lajugie 1, 2, * Sylvain Arlot 2, 1 Francis Bach 2, 1
* Auteur correspondant
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We consider unsupervised partitioning problems based explicitly or implicitly on the minimization of Euclidean distortions, such as clustering, image or video segmentation, and other change-point detection problems. We emphasize on cases with specific structure, which include many practical situations ranging from meanbasedchange-point detection to image segmentation problems. We aim at learning a Mahalanobis metric for these unsupervised problems, leading to feature weighting and/or selection. This is done in a supervised way by assuming the availability of several (partially) labeled datasets that share the same metric. We cast the metric learning problem as a large-margin structured prediction problem, with proper definition of regularizers and losses, leading to a convex optimization problem which can be solved efficiently. Our experiments show how learning the metric can significantlyimprove performance on bioinformatics, video or image segmentation problems.
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Communication dans un congrès
Proceedings of The 31st International Conference on Machine Learning, Jun 2014, Beijing, China
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Rémi Lajugie, Sylvain Arlot, Francis Bach. Large-Margin Metric Learning for Constrained Partitioning Problems. Proceedings of The 31st International Conference on Machine Learning, Jun 2014, Beijing, China. 〈hal-00796921〉

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