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In this paper, we propose an unifying view of several recently proposed structured sparsity-inducing norms.
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Learning with structured sparsity
J. Huang, T. Zhang, and D. Metaxas · 2011
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Convex and network flow optimization for structured sparsity
J. Mairal, R. Jenatton, G. Obozinski, and F. Bach · 2011
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Regularizers for structured sparsity
C.A. Micchelli, J.M. Morales, and M. Pontil · 2011
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V. Chandrasekaran, B. Recht, P.A. Parrilo, and A.S. Willsky · 2010
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Tree-guided group lasso for multi-task regression with structured sparsity
S. Kim and E. P. Xing · 2010
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A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
S. Negahban, P. Ravikumar, M.J. Wainwright, and B. Yu · 2010
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Structured variable selection with sparsity-inducing norms
R. Jenatton, J.Y. Audibert, and F. Bach
Cited in the paper.
Proximal methods for hierarchical sparse coding
R. Jenatton, J. Mairal, G. Obozinski, and F. Bach
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Group Lasso with overlaps: the Latent Group Lasso approach
G. Obozinski, L. Jacob, and J.-P. Vert · 2011
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Optimization with sparsity-inducing penalties
F. Bach, R. Jenatton, J. Mairal, and G. Obozinski · 2012
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Supervised feature selection in graphs with path coding penalties and network flows
J. Mairal and B. Yu · 2012
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