Search Machine Learning Repository: @inproceedings{icml2014c1_lajugie14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Large-Margin Metric Learning for Constrained Partitioning Problems},
    Url = {http://jmlr.org/proceedings/papers/v32/lajugie14.pdf},
    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 mean-based change-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 significantly improve performance on bioinformatics, video or image segmentation problems.},
    Author = {RĂ©mi Lajugie and Francis Bach and Sylvain Arlot},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {297-305}
   }