Probabilistic Segmentation via Total Variation Regularization

Abstract

We present a convex approach to probabilistic segmentation and modeling of time series data. Our approach builds upon recent advances in multivariate total variation regularization, and seeks to learn a separate set of parameters for the distribution over the observations at each time point, but with an additional penalty that encourages the parameters to remain constant over time. We propose efficient optimization methods for solving the resulting (large) optimization problems, and a two-stage procedure for estimating recurring clusters under such models, based upon kernel density estimation. Finally, we show on a number of real-world segmentation tasks, the resulting methods often perform as well or better than existing latent variable models, while being substantially easier to train.

References in corpus (4)

• Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
• The group fused Lasso for multiple change-point detection
• Joint modeling of multiple time series via the beta process with application to motion capture segmentation
• An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems