Probabilistic Segmentation via Total Variation Regularization

16 Nov 2015 Matt Wytock J. Zico Kolter

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... (read more)

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