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 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
Text
Hocking et al. "Learning Sparse Penalties for Change-Point Detection Using Max Margin Interval Regression." International Conference on Machine Learning, 2013.Markdown
[Hocking et al. "Learning Sparse Penalties for Change-Point Detection Using Max Margin Interval Regression." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/hocking2013icml-learning/)BibTeX
@inproceedings{hocking2013icml-learning,
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 = {International Conference on Machine Learning},
year = {2013},
pages = {172-180},
volume = {28},
url = {https://mlanthology.org/icml/2013/hocking2013icml-learning/}
}