Learning with structured sparsity

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 417-424

  Huang, Junzhou ^a   Zhang, Tong ^b   Metaxas, Dimitris ^a  

^a RUTGERS UNIVERSITY   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARBITRARY STRUCTURES; CODING COMPLEXITY; FEATURE SETS; GENERAL THEORY; GREEDY ALGORITHMS; LEARNING FORMULATION; NATURAL EXTENSION; SPARSITY PROBLEMS; STATISTICAL LEARNING;

EDUCATION; FLUORINE CONTAINING POLYMERS; ROBOT LEARNING;

STRUCTURED PROGRAMMING;

EID: 71149112556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Bach, F. R. (2008). Consistency of the group lasso and multiple kernel learning. Journal of Machine Learning Research, 9, 1179-1225.
2. Baraniuk, R., Cevher, V., Duarte, M., & Hegde, C. (2008). Model based compressive sensing. preprint.
3. Bradley Efron, Trevor Hastie, I. J., & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32, 407-499.
4. Candes, E. J., & Tao, T. (2005). Decoding by linear programming. IEEE Trans. on Information Theory, 51, 4203-4215.
5. Daudet, L. (2004). Sparse and structured decomposition of audio signals in overcomplete spaces. International Conference on Digital Audio Effects (pp. 1-5).
6. Donoho, D. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52, 1289-1306.
7. Grimm, D., Netzer, T., & Schweighofer, M. (2007). A note on the representation of positive polynomials with structured sparsity. Arch. Math., 89, 399-403.
8. Huang, J., & Zhang, T. (2009). The benefit of group sparsity (Technical Report). Rutgers University.
9. Huang, J., Zhang, T., & Metaxas, D. (2009). Learning with structured sparsity (Technical Report). Rutgers University. available from http://arxiv.org/abs/0903.3002.
10. Mallat, S. A Wavelet Tour of Signal Processing. Academic Press.
11. Needell, D., & Tropp, J. (2008). Cosamp: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis. Accepted.
12. Stojnic, M., Parvaresh, F., & Hassibi, B. (2008). On the reconstruction of block-sparse signals with an optimal number of measurements. Preprint.
13. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58, 267-288.
14. Tropp, J., & Gilbert, A. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53, 4655-4666.
15. Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of The Royal Statistical Society Series B, 68, 49-67.
16. Zhang, T. (2008). Adaptive forward-backward greedy algorithm for learning sparse representations. Proceedings of Neural Information Processing Systems (pp. 1-8).
17. Zhao, P., Rocha, G., & Yu, B. Grouped and hierarchical model selection through composite absolute penalties. The Annals of Statistics. to appear.