Efficient Euclidean projections in linear time

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

Volumn , Issue , 2009, Pages 657-664

  Liu, Jun ^a   Ye, Jieping ^a  

^a Arizona State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUXILIARY FUNCTIONS; BISECTION ALGORITHMS; BUILDING BLOCKES; CONVEX SET; EMPIRICAL STUDIES; EUCLIDEAN; EXISTING METHOD; LEARNING PROBLEM; LINEAR TIME; ROOT FINDING; SPECIAL STRUCTURE; TIME COMPLEXITY;

ALGORITHMS; COMPUTER SCIENCE; ROBOT LEARNING;

SET THEORY;

EID: 71149115443     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
2. Brent, R. (1971). An algorithm with guaranteed convergence for finding a zero of a function. Computer Journal, 14, 422-425.
3. Candès, E., & Wakin, M. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 25, 21-30.
4. Cormen, T., Leiserson, C., Rivest, R., & Stein, C. (2001). Introduction to algorithms. MIT Press.
5. Donoho, D. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52, 1289-1306.
6. 1-ball for learning in high dimensions. International Conference on Machine Learning (pp. 272-279).
7. Koh, K., Kim, S., & Boyd, S. (2007). An interior-point method for large-scale 11-regularized logistic regression. Journal of Machine Learning Research, 8, 1519-1555.
8. Nemirovski, A. (1994). Efficient methods in convex programming. Lecture Notes.
9. Nesterov, Y. (2003). Introductory lectures on convex optimization: A basic course. Kluwer Academic Publishers.
10. 2 regularization, and rotational invariance. International Conference on Machine Learning (pp. 78-85).
11. Shalev-Shwartz, S. (2007). Online learning: Theory, algorithms, and applications. Doctoral dissertation, Hebrew University.
12. Shalev-Shwartz, S., & Singer, Y (2006). Efficient learning of label ranking by soft projections onto polyhedra. Journal of Machine Learning Research, 7, 1567-1599.
13. 1-norm constraint and guarantees on sparsity (Technical Report). TTI.
14. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B, 58, 267-288.