Learning Sparse Penalties for Change-Point Detection using Max Margin Interval Regression - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2013

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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Dates and versions

hal-00824075 , version 1 (21-05-2013)

Identifiers

  • HAL Id : hal-00824075 , version 1

Cite

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, Supported by the International Machine Learning Society (IMLS), Jun 2013, Atlanta, United States. ⟨hal-00824075⟩
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