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

Summary 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.