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

Toby Hocking, Guillem Rigaill, Jean-Philippe Vert, Francis Bach
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):172-180, 2013.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-hocking13, title = {Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression}, author = {Hocking, Toby and Rigaill, Guillem and Vert, Jean-Philippe and Bach, Francis}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {172--180}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/hocking13.pdf}, url = {https://proceedings.mlr.press/v28/hocking13.html}, 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.} }
Endnote
%0 Conference Paper %T Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression %A Toby Hocking %A Guillem Rigaill %A Jean-Philippe Vert %A Francis Bach %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-hocking13 %I PMLR %P 172--180 %U https://proceedings.mlr.press/v28/hocking13.html %V 28 %N 3 %X 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.
RIS
TY - CPAPER TI - Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression AU - Toby Hocking AU - Guillem Rigaill AU - Jean-Philippe Vert AU - Francis Bach BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/26 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-hocking13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 3 SP - 172 EP - 180 L1 - http://proceedings.mlr.press/v28/hocking13.pdf UR - https://proceedings.mlr.press/v28/hocking13.html AB - 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. ER -
APA
Hocking, T., Rigaill, G., Vert, J. & Bach, F.. (2013). Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(3):172-180 Available from https://proceedings.mlr.press/v28/hocking13.html.

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