Catching Change-points with Lasso

We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise. Our approach consists in reframing this task in a variable selection context. We use a penalized least-squares criterion with a l1-type penalty for this purpose. We prove that, in an appropriate asymptotic framework, this method provides consistent estimators of the change-points. Then, we explain how to implement this method in practice by combining the LAR algorithm and a reduced version of the dynamic programming algorithm and we apply it to synthetic and real data.