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Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries.
“estimating the dimension of a model
G. Schwarz · 1978
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Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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Least angle regression
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani · 2004
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Introductory lectures on convex optimization : a basic course
Y. Nesterov · 2004
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Regularization and variable selection via the elastic net
H. Zou and T. Hastie · 2005
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An interior-point method for large-scale l1-regularized logistic regression
K. Koh, S. Kim, and S. Boyd · 2007
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Simultaneous regression shrinkage, variable selection and clustering of predictors with OSCAR
H. Bondell and B. Reich · 2008
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An introduction to compressive sampling
E. Candes and M. Wakin · 2008
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SLEP: Sparse Learning with Efficient Projections
J. Liu, S. Ji, and J. Ye · 2009
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Regularization paths for generalized linear models via coordinate descent
J. H. Friedman, T. Hastie, and R. Tibshirani · 2010
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Learning sparse representations of high dimensional data on large scale dictionaries
J. X. Zhen, X. Hao, and J. R. Peter · 2011
Cited alongside, same era.
Safe feature elimination in sparse supervised learning
L. Ghaoui, V. Viallon, and T. Rabbani · 2012
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Strong rules for discarding predictors in lasso-type problems
R. Tibshirani, J. Bien, J. H. Friedman, T. Hastie, N. Simon, J. Taylor, and R. J. Tibshirani · 2012
Later among the works it cites.
Gradient methods for minimizing composite objective function
Y. Nesterov · 2013
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Safe screening of non-support vectors in pathwise SVM computation
K. Ogawa, Y. Suzuki, and I. Takeuchi · 2013
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Lasso screening rules via dual polytope projection
J. Wang, B. Lin, P. Gong, P. Wonka, and J. Ye · 2013
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