Uncertainty in Artificial Intelligence
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Fast Newton methods for the group fused lasso
Matt Wytock, Suvrit Sra, J. Kolter
Abstract:
We present a new algorithmic approach to the group fused lasso, a convex model that approx- imates a multi-dimensional signal via an ap- proximately piecewise-constant signal. This model has found many applications in mul- tiple change point detection, signal compres- sion, and total variation denoising, though existing algorithms typically using first-order or alternating minimization schemes. In this paper we instead develop a specialized pro- jected Newton method, combined with a pri- mal active set approach, which we show to be substantially faster that existing methods. Furthermore, we present two applications that use this algorithm as a fast subroutine for a more complex outer loop: segmenting linear regression models for time series data, and color image denoising. We show that on these problems the proposed method performs very well, solving the problems faster than state- of-the-art methods and to higher accuracy.
Keywords:
Pages: 888-897
PS Link:
PDF Link: /papers/14/p888-wytock.pdf
BibTex:
@INPROCEEDINGS{Wytock14,
AUTHOR = "Matt Wytock and Suvrit Sra and J. Kolter",
TITLE = "Fast Newton methods for the group fused lasso",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "888--897"
}


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