Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):172-180, 2013.
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.