Fast Projections onto ℓ1, q -Norm Balls for Grouped Feature Selection
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[1] Stéphane Canu,et al. $\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning , 2011, IEEE Transactions on Neural Networks.
[2] R. Tyrrell Rockafellar,et al. Convex Analysis , 1970, Princeton Landmarks in Mathematics and Physics.
[3] Inderjit S. Dhillon,et al. A scalable trust-region algorithm with application to mixed-norm regression , 2010, ICML.
[4] Shie-Shien Yang. [Multiresponse Estimation with Special Application to Linear Systems of Differential Equations]: Discussion , 1985 .
[5] Roger Fletcher,et al. Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming , 2005, Numerische Mathematik.
[6] Patrick L. Combettes,et al. Proximal Splitting Methods in Signal Processing , 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.
[7] R. Tibshirani,et al. A note on the group lasso and a sparse group lasso , 2010, 1001.0736.
[8] Stephen J. Wright,et al. Simultaneous Variable Selection , 2005, Technometrics.
[9] David L. Donoho,et al. De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.
[10] Jieping Ye,et al. Efficient L1/Lq Norm Regularization , 2010, ArXiv.
[11] Jun Liu,et al. Efficient Euclidean projections in linear time , 2009, ICML '09.
[12] K. Kiwiel. On Linear-Time Algorithms for the Continuous Quadratic Knapsack Problem , 2007 .
[13] C. Michelot. A finite algorithm for finding the projection of a point onto the canonical simplex of ∝n , 1986 .
[14] Dimitri P. Bertsekas,et al. Nonlinear Programming , 1997 .
[15] J. Tropp. Algorithms for simultaneous sparse approximation. Part II: Convex relaxation , 2006, Signal Process..
[16] Han Liu,et al. Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery , 2009, ICML '09.
[17] Julien Mairal,et al. Proximal Methods for Sparse Hierarchical Dictionary Learning , 2010, ICML.
[18] Francis R. Bach,et al. Structured sparsity-inducing norms through submodular functions , 2010, NIPS.
[19] Timo Similä,et al. Input selection and shrinkage in multiresponse linear regression , 2007, Comput. Stat. Data Anal..
[20] Michael I. Jordan,et al. Multi-task feature selection , 2006 .
[21] Jieping Ye,et al. Moreau-Yosida Regularization for Grouped Tree Structure Learning , 2010, NIPS.
[22] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[23] Mark W. Schmidt,et al. GROUP SPARSITY VIA LINEAR-TIME PROJECTION , 2008 .
[24] Stephen J. Wright,et al. Optimization for Machine Learning , 2013 .
[25] Francis R. Bach,et al. Consistency of the group Lasso and multiple kernel learning , 2007, J. Mach. Learn. Res..
[26] Yoram Singer,et al. Efficient Online and Batch Learning Using Forward Backward Splitting , 2009, J. Mach. Learn. Res..
[27] P. Zhao,et al. The composite absolute penalties family for grouped and hierarchical variable selection , 2009, 0909.0411.
[28] Julien Mairal,et al. Convex optimization with sparsity-inducing norms , 2011 .
[29] J. Borwein,et al. Two-Point Step Size Gradient Methods , 1988 .
[30] Massimiliano Pontil,et al. Regularized multi--task learning , 2004, KDD.
[31] M. Kowalski. Sparse regression using mixed norms , 2009 .
[32] Qian Xu,et al. Probabilistic Multi-Task Feature Selection , 2010, NIPS.
[33] José Mario Martínez,et al. Nonmonotone Spectral Projected Gradient Methods on Convex Sets , 1999, SIAM J. Optim..
[34] Mark W. Schmidt,et al. Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm , 2009, AISTATS.
[35] Trevor Darrell,et al. An efficient projection for l1, ∞ regularization , 2009, ICML '09.
[36] Jun Liu,et al. Efficient `1=`q Norm Regularization , 2010 .
[37] Charles A. Micchelli,et al. Learning Multiple Tasks with Kernel Methods , 2005, J. Mach. Learn. Res..
[38] Trevor Darrell,et al. An efficient projection for l 1 , infinity regularization. , 2009, ICML 2009.
[39] A. Fiacco. A Finite Algorithm for Finding the Projection of a Point onto the Canonical Simplex of R " , 2009 .
[40] Julien Mairal,et al. Network Flow Algorithms for Structured Sparsity , 2010, NIPS.
[41] Michael Patriksson,et al. A Survey on a Classic Core Problem in Operations Research , 2005 .
[42] Shuiwang Ji,et al. SLEP: Sparse Learning with Efficient Projections , 2011 .