Identification of switched linear regression models using sum-of-norms regularization

This paper proposes a general convex framework for the identification of switched linear systems. The proposed framework uses over-parameterization to avoid solving the otherwise combinatorially forbidding identification problem and takes the form of a least-squares problem with a sum-of-norms regularization, a generalization of the `1-regularization. The regularization constant regulates complexity and is used to trade off fit and the number of submodels.