Convex relaxations of penalties for sparse correlated variables with bounded total variation

DOI10.1007/s10994-015-5511-2zbMath1341.62114OpenAlexW850425088WikidataQ57317209 ScholiaQ57317209MaRDI QIDQ747277

Andreas Argyriou, Gaël Varoquaux, Eugene Belilovsky, Matthew B. Blaschko

Publication date: 23 October 2015

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-015-5511-2

zbMATH Keywords

total variationfeature selectionstructured sparsitybrain decoding\(k\)-support

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55)

Uses Software

• MNIST

Cites Work

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• Nonlinear total variation based noise removal algorithms
• Introductory lectures on convex optimization. A basic course.
• The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods
• A generalized solution of the orthogonal Procrustes problem
• Optimization with Sparsity-Inducing Penalties
• Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows
• Sparsity and Smoothness Via the Fused Lasso
• Divergence Function, Duality, and Convex Analysis
• Regularization and Variable Selection Via the Elastic Net
• Excessive Gap Technique in Nonsmooth Convex Minimization
• Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
• Learning with Structured Sparsity
• Convex analysis and monotone operator theory in Hilbert spaces

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