High-Dimensional Fused Lasso Regression Using Majorization-Minimization and Parallel Processing

In this article, we propose a majorization-minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices...

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Veröffentlicht in:Journal of computational and graphical statistics Jg. 24; H. 1; S. 121 - 153
Hauptverfasser: Yu, Donghyeon, Won, Joong-Ho, Lee, Taehoon, Lim, Johan, Yoon, Sungroh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria Taylor & Francis 02.01.2015
American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Taylor & Francis Ltd
Schlagworte:
Algorithms
Circuits
Design engineering
Graphics processing unit
Majorization-minimization algorithm
Parallel computation
Parallel processing
Regression Analysis
Studies
ISSN:1061-8600, 1537-2715
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Beschreibung
Zusammenfassung:In this article, we propose a majorization-minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices and penalty structures within a few tens of iterations. We also show that the convergence of the proposed algorithm is guaranteed. We conduct numerical studies to compare our algorithm with other existing algorithms, demonstrating that the proposed MM algorithm is competitive in many settings including the two-dimensional FLR with arbitrary design matrices. The merit of GPU parallelization is also exhibited. Supplementary materials are available online.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2013.878662