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Classification with a sparsity constraint on the solution plays a central role in many high dimensional machine learning applications.
Ridge regression: Biased estimation for nonorthogonal problems
Arthur E Hoerl and Robert W Kennard · 1970
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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A gene-expression signature as a predictor of survival in breast cancer
Marc J Van De Vijver, Yudong D He, Laura J van’t Veer, Hongyue Dai, Augustinus AM Hart, Dorien W Voskuil, George J Schreiber, Johannes L Peterse, Chris Roberts, Matthew J Marton, et al · 2002
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Using machine learning to detect cognitive states across multiple subjects
Xuerui Wang, Tom M Mitchell, and Rebecca Hutchinson · 2003
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Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, Scott L Pomeroy, Todd R Golub, Eric S Lander, et al · 2005
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Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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Model selection and estimation in regression with grouped variables
Ming Yuan and Yi Lin · 2006
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The neural bases of the short-term storage of verbal information are anatomically variable across individuals
Eva Feredoes, Giulio Tononi, and Bradley R Postle · 2007
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Honest variable selection in linear and logistic regression models via ℓ 1 \ell_{1} and ℓ 1 + ℓ 2 \ell_{1}+\ell_{2} penalization
Florentina Bunea · 2008
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The group lasso for logistic regression
Lukas Meier, Sara Van De Geer, and Peter Bühlmann · 2008
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Group lasso with overlap and graph lasso
Laurent Jacob, Guillaume Obozinski, and Jean-Philippe Vert · 2009
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Taking advantage of sparsity in multi-task learning
Karim Lounici, Massimiliano Pontil, Alexandre B Tsybakov, and Sara Van De Geer · 2009
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On the reconstruction of block-sparse signals with an optimal number of measurements
Mihailo Stojnic, Farzad Parvaresh, and Babak Hassibi · 2009
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Interpreting single trial data using groupwise regularisation
Marcel van Gerven, Christian Hesse, Ole Jensen, and Tom Heskes · 2009
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Self-concordant analysis for logistic regression
Francis Bach · 2010
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A dirty model for multi-task learning
Ali Jalali, Pradeep D Ravikumar, Sujay Sanghavi, and Chao Ruan · 2010
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A primal-dual algorithm for group sparse regularization with overlapping groups
Proximal methods for hierarchical sparse coding
Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, and Francis Bach · 2011
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Group lasso with overlaps: the latent group lasso approach
Guillaume Obozinski, Laurent Jacob, and Jean-Philippe Vert · 2011
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Convex approaches to model wavelet sparsity patterns
Nikhil S Rao, Robert D Nowak, Stephen J Wright, and Nick G Kingsbury · 2011
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C-hilasso: A collaborative hierarchical sparse modeling framework
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapiro, and Yonina C Eldar · 2011
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Structured sparsity and generalization
Andreas Maurer and Massimiliano Pontil · 2012
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A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
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Sofia Mosci, Silvia Villa, Alessandro Verri, and Lorenzo Rosasco · 2010
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Restricted eigenvalue properties for correlated gaussian designs
Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2010
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Sparse logistic regression for whole brain classification of fmri data
Srikanth Ryali, Kaustubh Supekar, Daniel A Abrams, and Vinod Menon · 2010
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Collaborative hierarchical sparse modeling
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapiro, and Yonina Eldar · 2010
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Exclusive lasso for multi-task feature selection
Yang Zhou, Rong Jin, and Steven Hoi · 2010
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Sparse group lasso for regression on land climate variables
Soumyadeep Chatterjee, Arindam Banerjee, Snigdhansu Chatterjee, and Auroop R Ganguly · 2011
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Sahand N Negahban, Pradeep Ravikumar, Martin J Wainwright, Bin Yu, et al · 2012
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Universal measurement bounds for structured sparse signal recovery
Nikhil S Rao, Ben Recht, and Robert D Nowak · 2012
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Sparse regression analysis of task-relevant information distribution in the brain
Irina Rish, Guillermo A Cecchia, Kyle Heutonb, Marwan N Balikic, and A Vania Apkarianc · 2012
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Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach
Yaniv Plan and Roman Vershynin · 2013
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Sparse overlapping sets lasso for multitask learning and its application to fmri analysis
Nikhil Rao, Christopher Cox, Rob Nowak, and Timothy T Rogers · 2013
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A sparse-group lasso
Noah Simon, Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2013
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