Learning Sparse Penalties for Change-Point Detection using Max Margin Interval Regression

Abstract : In segmentation models, the number of change-points is typically chosen using a pe- nalized cost function. In this work, we pro- pose to learn the penalty and its constants in databases of signals with weak change-point annotations. We propose a convex relaxation for the resulting interval regression problem, and solve it using accelerated proximal gra- dient methods. We show that this method achieves state-of-the-art change-point detec- tion in a database of annotated DNA copy number profiles from neuroblastoma tumors.
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Communication dans un congrès
ICML 2013 - 30 th International Conference on Machine Learning, Jun 2013, Atlanta, United States. 2013
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https://hal.inria.fr/hal-00824075
Contributeur : Toby Dylan Hocking <>
Soumis le : mardi 21 mai 2013 - 06:50:28
Dernière modification le : lundi 18 juin 2018 - 10:32:23

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  • HAL Id : hal-00824075, version 1

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Guillem Rigaill, Toby Dylan Hocking, Francis Bach, Jean-Philippe Vert. Learning Sparse Penalties for Change-Point Detection using Max Margin Interval Regression. ICML 2013 - 30 th International Conference on Machine Learning, Jun 2013, Atlanta, United States. 2013. 〈hal-00824075〉

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