Large-Margin Metric Learning for Constrained Partitioning ProblemsReport as inadecuate

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* Corresponding author 1 DI-ENS - Département d-informatique de l-École normale supérieure 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.

Keywords : Machine Learning Metric Learning Change-point detection Clustering Image Segmentation

Author: Rémi Lajugie - Sylvain Arlot - Francis Bach -



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