Citation: |
[1] |
R. K. Ando and T. Zhang, A framework for learning predictive structures from multiple tasks and unlabeleddata, Journal of Machine Learning Research, 6 (2005), 1817-1853. |
[2] |
A. Argyriou, T. Evgeniou and M. Pontil, Convex multi-convex feature learning, Machine Learning, 73 (2008), 243-272. |
[3] |
B. Bakker and T. Heskes, Task clustering and gating for Bayesian multi-task learning, Journal of Machine Learning Research, 4 (2003), 83-99. |
[4] |
S. Chen, D. Donoho and M. Saunders, Atomic decomposition by basis pursuit, SIAM Journal on Scientific Computing, 20 (1999), 33-61.doi: 10.1137/S1064827596304010. |
[5] |
J. Duchi and Y. Singer, Efficient online and batch learning using forward backward splitting, Journal of Machine Learning Research, 10 (2009), 2899-2934. |
[6] |
T. Evgeniou, C. A. Micchelli and M. Pontil, Learning multiple tasks with kernel methods, Journal of Machine Learning Research, 6 (2005), 615-637. |
[7] |
D. Gabay and B. Mercier, A dual algorithm for the solution of nonlinear variational problems via finite-element approximations, Computers & Mathematics with Applications, 2 (1976), 17-40. |
[8] |
R. Glowinski, "Numerical Methods for Nonlinear Variational Problems," Springer, New York, 1984. |
[9] |
R. Glowinski and A. Marrocco, Sur l'approximation, par élémentsfinis d'ordre un, et la résolution, parpénalisation-dualité d'une classe de problèmes deDirichlet nonlinéaires, Revue Francaise d'automatique, informatique, recherche opéretionnelle. Analyse numérique, 2 (1975), 41-76. |
[10] |
B. He, L. Z. Liao, D. Han and H. Yang, A new inexact alternating directions method for monotone variational inequalities, Mathematical Programming, 92 (2002), 103-118.doi: 10.1007/s101070100280. |
[11] |
B. He, S. L. Wang and H. Yang, A modified variable-penalty alternating directions method for monotone variational inequalities, Journal of Computational Mathematics, 21 (2003), 495-504. |
[12] |
J. Liu, J. Chen and J. Ye, "Large-Scale Sparse Logistic Regression," in "ACM SIGKDD International Conference On KnowledgeDiscovery and Data Mining", 2009. |
[13] |
J. Liu, S. Ji and J. Ye, "Multi-Task Feather Learning Via Efficient $l_{2,1}$-norm Minimization," in "Comference on Uncertainty in Artificial Intelligence", 2009. |
[14] |
M. Kowalski, Sparse regression using mixednorms, Applied and Computational Harmonic Analysis, 27 (2009), 303-324.doi: 10.1016/j.acha.2009.05.006. |
[15] |
M. Kowalski, M. Szafranski and L. Ralaivola, "Multiple Indefinite Kernel Learning with Mixed Normregularization," Proceedings of the 26th Annual International Conference on Machine Learning, 2009. |
[16] |
A. Nemirovski, "Efficient Methods in Convex Programming," Lecture Notes, 1994. |
[17] |
Y. Nesterov, "Introductory Lectures on Convex Optimization: A Basic Course," Kluwer Academic Publishers, 2003. |
[18] |
Y. Nesterov, "Gradient Methods for Minimizing Composite Objective Function," CORE report, 2007; available at http://www.ecore.be/DPs/dp_1191313936.pdf. |
[19] |
F. Nie, H. Huang, X. Cai and C. Ding, "Efficient and Robust Feature Selection via Joint $l_{2,1}$-Normsminimization," Neural Information Processing Systems Foundation, 2010. |
[20] |
G. Obozinski, B. Taskar and M. I. Jordan, "Multi-Task Feature Selection," Technical Report, UC Berkeley, 2006. |
[21] |
Y. Saeys, I. Inza and P. Larranaga, A review of feature selection techniques in bioinformatics, Bioinformatics, 23 (2007), 2507-2517.doi: 10.1093/bioinformatics/btm344. |
[22] |
Y. Xiao, S.-Y. Wu and D.-H. Li, Splitting and linearizing augmented Lagrangian algorithm for subspace recovery from corrupted observations, Adv. Comput. Math., DOI 10.1007/s10444-011-9261-9. |
[23] |
T. Xiong, J. Bi, B. Rao and V. Cherkassky, "Probabilistic Joint Feature Selection for Multi-Task Learning," in "SIAM International Conference on Data Mining", 2006. |
[24] |
M. H. Xu, Proximal alternating directions method for structured variational inequalities, Journal of Optimization Theory and Applications, 134 (2007), 107-117.doi: 10.1007/s10957-007-9192-2. |
[25] |
J. Yang, Dynamic power price problem: An inverse variational inequality approach, Journal of Industrial and Management Optimization, 4 (2008), 673-684. |
[26] |
J. Yang and X. Yuan, Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization, Math. Comput. doi: 10.1090/S0025-5718-2012-02598-1. |
[27] |
J. Yang and Y. Zhang, Alternating direction algorithms for $l_1$-problemsin compressive sensing, SIAM Journal on Scientific Computing, 33 (2011), 250-278.doi: 10.1137/090777761. |
[28] |
J. Zhang, Z. Ghahramani and Y. Yang, Flexible latent variable models for multi-task learning, Machine Learning, 73 (2008), 221-242. |