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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Bibliographic Details
Published in:Journal of computational and graphical statistics Vol. 24; no. 1; pp. 121 - 153
Main Authors: Yu, Donghyeon, Won, Joong-Ho, Lee, Taehoon, Lim, Johan, Yoon, Sungroh
Format: Journal Article
Language:English
Published: Alexandria Taylor & Francis 02.01.2015
American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Taylor & Francis Ltd
Subjects:
Algorithms
Circuits
Design engineering
Graphics processing unit
Majorization-minimization algorithm
Parallel computation
Parallel processing
Regression Analysis
Studies
ISSN:1061-8600, 1537-2715
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2013.878662