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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Veröffentlicht in:Journal of computational and graphical statistics Jg. 22; H. 2; S. 246 - 260
1. Verfasser: Johnson, Nicholas A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 2013
Schlagworte:
Optimization
Signal processing
Total variation denoising
Viterbi
ISSN:1061-8600, 1537-2715
Online-Zugang:Volltext
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Zusammenfassung: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.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2012.681238