The Group-Lasso: ℓ1,∞ regularization versus ℓ1,2 regularization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6376 LNCS, Issue , 2010, Pages 252-261

  Vogt, Julia E ^a   Roth, Volker ^a  

^a UNIVERSITY OF BASEL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE SETS; APPLICATION SCENARIO; INPUT DIMENSIONS; LASSO CONSTRAINT; LASSO METHOD; LEARNING PROBLEM; MULTITASK LEARNING; PREDICTION ACCURACY; PREDICTION PROBLEM; UNIQUENESS OF SOLUTION;

PATTERN RECOGNITION;

EID: 78349232078     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15986-2_26     Document Type: Conference Paper

References (14)

1. Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. Roy. Stat. Soc. B 58(1), 267-288 (1996)
2. Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc. B, 49-67 (2006)
3. Turlach, B.A., Venables,W.N., Wright, S.J.: Simultaneous variable selection. Technometrics 47, 349-363 (2005)
4. Meier, L., van de Geer, S., Bühlmann, P.: The Group Lasso for Logistic Regression. J. Roy. Stat. Soc. B 70(1), 53-71 (2008)
5. Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. In: Advances in Neural Information Processing Systems, vol. 19. MIT Press, Cambridge (2007)
6. Kim, Y., Kim, J., Kim, Y.: Blockwise sparse regression. Statistica Sinica 16, 375-390 (2006)
7. Roth, V., Fischer, B.: The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms. In: ICML 2008, pp. 848-855. ACM, New York (2008)
8. Schmidt, M., Murphy, K., Fung, G., Rosales, R.: Structure learning in random fields for heart motion abnormality detection. In: CVPR (2008)
9. 1∞ regularization. In: 26th Intern. Conference on Machine Learning (2009)
10. Liu, H., Palatucci, M., Zhang, J.: Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. In: 26th Intern. Conference on Machine Learning (2009)
11. Osborne, M., Presnell, B., Turlach, B.: On the LASSO and its dual. J. Comp. and Graphical Statistics 9(2), 319-337 (2000)
12. McCullaghand, P., Nelder, J.: Generalized Linear Models. Chapman & Hall, Boca Raton (1983)
13. Liu, Q., Xu, Q., Zheng, V.W., Xue, H., Cao, Z., Yang, Q.: Multi-task learning for cross-platform sirna efficacy prediction: an in-silico study. BMC Bioinformatics 11(1), 181 (2010)
14. Yeo, G., Burge, C.: Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comp. Biology 11, 377-394 (2004)