Learning sparse penalties for change-point detection using max margin interval regression

Guillem Rigaill, Toby Dylan Hocking, Francis Bach, Jean Philippe Vert

Research output: Contribution to conferencePaperpeer-review

10 Scopus citations

Abstract

In segmentation models, the number of change-points is typically chosen using a penalized cost function. In this work, we propose 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 gradient methods. We show that this method achieves state-of-the-art change-point detection in a database of annotated DNA copy number profiles from neuroblastoma tumors.

Original languageEnglish (US)
Pages1209-1217
Number of pages9
StatePublished - 2013
Externally publishedYes
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: Jun 16 2013Jun 21 2013

Conference

Conference30th International Conference on Machine Learning, ICML 2013
Country/TerritoryUnited States
CityAtlanta, GA
Period6/16/136/21/13

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Sociology and Political Science

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