Tight convex relaxations for sparse matrix factorization
暂无分享,去创建一个
Jean-Philippe Vert | Guillaume Obozinski | Emile Richard | G. Obozinski | Jean-Philippe Vert | E. Richard
[1] Christos Thrampoulidis,et al. The squared-error of generalized LASSO: A precise analysis , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[2] Mark Jerrum,et al. Large Cliques Elude the Metropolis Process , 1992, Random Struct. Algorithms.
[3] R. Tibshirani,et al. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. , 2009, Biostatistics.
[4] Shai Avidan,et al. Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms , 2005, NIPS.
[5] Joel A. Tropp,et al. Living on the edge: A geometric theory of phase transitions in convex optimization , 2013, ArXiv.
[6] R. Tibshirani,et al. Sparse Principal Component Analysis , 2006 .
[7] Paul Tseng,et al. A coordinate gradient descent method for nonsmooth separable minimization , 2008, Math. Program..
[8] B. Moghaddam,et al. Sparse regression as a sparse eigenvalue problem , 2008, 2008 Information Theory and Applications Workshop.
[9] Nathan Srebro,et al. Sparse Prediction with the $k$-Support Norm , 2012, NIPS.
[10] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.
[11] Marc Teboulle,et al. Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint , 2011, SIAM Rev..
[12] Nicolas Vayatis,et al. Estimation of Simultaneously Sparse and Low Rank Matrices , 2012, ICML.
[13] Jean Ponce,et al. Convex Sparse Matrix Factorizations , 2008, ArXiv.
[14] Lester W. Mackey,et al. Deflation Methods for Sparse PCA , 2008, NIPS.
[15] Rina Foygel,et al. Corrupted Sensing: Novel Guarantees for Separating Structured Signals , 2013, IEEE Transactions on Information Theory.
[16] Xiao-Tong Yuan,et al. Truncated power method for sparse eigenvalue problems , 2011, J. Mach. Learn. Res..
[17] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[18] Xuan Vinh Doan,et al. Finding approximately rank-one submatrices with the nuclear norm and l1 norm , 2010, 1011.1839.
[19] Rajat Raina,et al. Efficient sparse coding algorithms , 2006, NIPS.
[20] Babak Hassibi,et al. Asymptotically Exact Denoising in Relation to Compressed Sensing , 2013, ArXiv.
[21] Stéphane Gaïffas,et al. Link prediction in graphs with autoregressive features , 2012, J. Mach. Learn. Res..
[22] Yonina C. Eldar,et al. Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices , 2012, IEEE Transactions on Information Theory.
[23] R. Bhatia. Matrix Analysis , 1996 .
[24] V. Koltchinskii,et al. Nuclear norm penalization and optimal rates for noisy low rank matrix completion , 2010, 1011.6256.
[25] Julien Mairal,et al. Structured sparsity through convex optimization , 2011, ArXiv.
[26] G. Watson. Characterization of the subdifferential of some matrix norms , 1992 .
[27] Guillermo Sapiro,et al. Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..
[28] Alexandre d'Aspremont,et al. Optimal Solutions for Sparse Principal Component Analysis , 2007, J. Mach. Learn. Res..
[29] J. T. Chu. On bounds for the normal integral , 1955 .
[30] Martin J. Wainwright,et al. Information-Theoretic Limits on Sparsity Recovery in the High-Dimensional and Noisy Setting , 2007, IEEE Transactions on Information Theory.
[31] B. Nadler,et al. Do Semidefinite Relaxations Really Solve Sparse PCA , 2013 .
[32] Jean-Philippe Vert,et al. Group lasso with overlap and graph lasso , 2009, ICML '09.
[33] Emmanuel J. Candès,et al. PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming , 2011, ArXiv.
[34] Yurii Nesterov,et al. Generalized Power Method for Sparse Principal Component Analysis , 2008, J. Mach. Learn. Res..
[35] Michael I. Jordan,et al. A Direct Formulation for Sparse Pca Using Semidefinite Programming , 2004, SIAM Rev..
[36] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[37] Julien Mairal,et al. Optimization with Sparsity-Inducing Penalties , 2011, Found. Trends Mach. Learn..
[38] Francis R. Bach,et al. Intersecting singularities for multi-structured estimation , 2013, ICML.
[39] Emmanuel J. Candès,et al. How well can we estimate a sparse vector? , 2011, ArXiv.
[40] 丸山 徹. Convex Analysisの二,三の進展について , 1977 .
[41] S. Szarek,et al. Chapter 8 - Local Operator Theory, Random Matrices and Banach Spaces , 2001 .
[42] M. Wainwright,et al. High-dimensional analysis of semidefinite relaxations for sparse principal components , 2008, 2008 IEEE International Symposium on Information Theory.
[43] Francis R. Bach,et al. Convex relaxations of structured matrix factorizations , 2013, ArXiv.
[44] Michael I. Jordan,et al. Computational and statistical tradeoffs via convex relaxation , 2012, Proceedings of the National Academy of Sciences.
[45] G. Jameson. Summing and nuclear norms in Banach space theory , 1987 .
[46] Philippe Rigollet,et al. Complexity Theoretic Lower Bounds for Sparse Principal Component Detection , 2013, COLT.
[47] Joel A. Tropp,et al. Living on the edge: phase transitions in convex programs with random data , 2013, 1303.6672.