Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Fast Newton methods for the group fused lasso
Matt Wytock, Suvrit Sra, J. Kolter
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.
Pages: 888-897
PS Link:
PDF Link: /papers/14/p888-wytock.pdf
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)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "888--897"

hosted by DSL   •   site info   •   help