High-Dimensional Fused Lasso Regression Using Majorization-Minimization and Parallel Processing
High-Dimensional Fused Lasso Regression Using Majorization-Minimization and Parallel Processing
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Alexandria: Taylor & Francis
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English
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Alexandria: Taylor & Francis
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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 itera...
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Full title
High-Dimensional Fused Lasso Regression Using Majorization-Minimization and Parallel Processing
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TN_cdi_proquest_journals_1673949910
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_1673949910
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ISSN
1061-8600
E-ISSN
1537-2715
DOI
10.1080/10618600.2013.878662
