A path algorithm for the fused lasso signal approximator

Journal of Computational and Graphical Statistics

Volumn 19, Issue 4, 2010, Pages 984-1006

  Hoefling, Holger ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Convex optimization; Lasso; Penalized regression

Indexed keywords

EID: 79952801173     PISSN: 10618600     EISSN: None     Source Type: Journal    
DOI: 10.1198/jcgs.2010.09208     Document Type: Article

References (15)

1. Bertsekas, D. P. (1999), Nonlinear Programming, Nashua, NH: Athena Scientific [991]
2. Cormen, T. H., Leierson, C E., Rivest, R. L., and Stein, C (2001), Introduction to Algorithms (2nd ed.), New York: MIT Press/McGraw-Hill. [993]
3. Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), "Least Angle Regression" (with discussion), The Annals of Statistics, 32 (2), 407-499. [984]
4. Friedman, J., Hastie, T., Hoefling, H., and Tibshirani, R. (2007), "Pathwise Coordinate Optimization," The Annals ofAppliedStatistics, 2 (1), 302-332. [987,1000,1003-1005]
5. Grant, M., and Boyd, S. (2008a), "CVX: Matlab Software for Disciplined Convex Programming," available at http://stanford.edu/~boyd/ cvx. [1000,1004]
6. (2008b), Graph Implementations for Nonsmooth Convex Programs. Recent Advances in Learning and Control (tribute to M. Vidyasagar), London: Springer. [1000,1004]
7. Hastie, T., Rosset, S., Tibshirani, R., and Zhu, J. (2004), "The Entire Regularization Path for the Support Vector Machine," The Journal ofMachine Learning Research, 5, 1391-1415. [985]
8. Jungnickel, D. (2007), Graphs, Networks and Algorithms (3rd ed.), Berlin: Springer. [990,993]
9. Osborne, M. R., Presnell, B., and Turlach, B. A. (2000), "A New Approach to Variable Selection in Least Squares Problems," IMA Journal of Numerical Analysis, 20, 389-404. [984]
10. Park, M.-Y., and Hastie, T (2007), "An L1 Regularization-Path Algorithm for Generalized Linear Models," Journal of the Royal Statistical Society, Ser B, 69, 659-677. [985]
11. Pollak, I., Willsky, A. S., and Huang, Y (2005), "Nonlinear Evolution Equations as Fast and Exact Solvers of Estimation Problems," IEEE Transactions on Signal Processing, 53, 484-498. [986] (Pubitemid 40181745)
12. Rosset, S., and Zhu, J. (2007), "Piecewise Linear Regularized Solution Paths," The Annals of Statistics, 35 (3), 1012-1030. [985]
13. Sleator, D. D., and Tarjan, R. E. (1983), "A Data Structure for Dynamic Trees," Journal of Journal of Computer andSystem Sciences, 24, 362-391. [998]
14. Tibshirani, R. (1996), "Regression Shrinkage and Selection via the Lasso," Journal of the Royal Statistical Society Ser. B, 58, 267-288. [984]
15. Tibshirani, R., Saunders, M., Zhu, J., and Rosset, S. (2005), "Sparsity and Smoothness via the Fused Lasso," Journal of the Royal Statistical Society, Ser. B, 67, 91-108. [985] (Pubitemid 40167043)