A Dynamic Programming Algorithm for the Fused Lasso and L 0-Segmentation

We propose a dynamic programming algorithm for the one-dimensional Fused Lasso Signal Approximator (FLSA). The proposed algorithm has a linear running time in the worst case. A similar approach is developed for the task of least squares segmentation, and simulations indicate substantial performance...

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Published in:Journal of computational and graphical statistics Vol. 22; no. 2; pp. 246 - 260
Main Author: Johnson, Nicholas A.
Format: Journal Article
Language:English
Published: Taylor & Francis 2013
Subjects:
Optimization
Signal processing
Total variation denoising
Viterbi
ISSN:1061-8600, 1537-2715
Online Access:Get full text
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Abstract We propose a dynamic programming algorithm for the one-dimensional Fused Lasso Signal Approximator (FLSA). The proposed algorithm has a linear running time in the worst case. A similar approach is developed for the task of least squares segmentation, and simulations indicate substantial performance improvement over existing algorithms. Examples of R and C implementations are provided in the online Supplementary materials, posted on the journal web site.
AbstractList We propose a dynamic programming algorithm for the one-dimensional Fused Lasso Signal Approximator (FLSA). The proposed algorithm has a linear running time in the worst case. A similar approach is developed for the task of least squares segmentation, and simulations indicate substantial performance improvement over existing algorithms. Examples of R and C implementations are provided in the online Supplementary materials, posted on the journal web site.
Author Johnson, Nicholas A.
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Cites_doi 10.1109/5.18626
10.1137/070690274
10.1109/TSP.2004.840786
10.1093/biostatistics/kxm013
10.1145/366573.366611
10.1016/j.csda.2008.08.005
10.1214/07-AOAS131
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SubjectTerms Optimization
Signal processing
Total variation denoising
Viterbi
Title A Dynamic Programming Algorithm for the Fused Lasso and L 0-Segmentation
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