Efficient generalized fused lasso and its application to the diagnosis of Alzheimer's disease

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2014, Pages 2163-2169

  Xin, Bo ^a   Kawahara, Yoshinobu ^b   Wang, Yizhou ^a   Gao, Wen ^a  

^a Peking University   (China)

^b OSAKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALZHEIMER'S DISEASE; FUSED LASSOS; ITS APPLICATIONS;

EID: 84908154815     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Ashburner, J. 2007. A fast diffeomorphic image registration algorithm. Neuroimage 38(1):95-113.
2. Azencott, C.; Grimm, D.; Sugiyama, M.; Kawahara, Y.; and Borgwardt, K. 2013. Efficient network-guided multilocus association mapping with graph cuts. Bioinformatics 29(13): il71-il79.
3. Bach, F.; Jenatton, R.; Mairal, J.; and Obozinski, G. 2012. Optimization with sparsity-inducing penalties. Foundations and Trends in Machine Learning 4(1):1-106.
4. Bach, F. 2010. Structured sparsity-inducing norms through submodular functions. In Advances in Neural Information Processing Systems, volume 23. 118-126.
5. Beck, A., and Teboulle, M. 2009. A fast iterative shrinkagethresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences 2(1):183-202.
6. Cheng, B.; Zhang, D.; and Shen, D. 2012. Domain transfer learning for mci conversion prediction. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012. Springer. 82-90.
7. Chu, C.; Hsu, A.-L.; Chou, K.-H.; Bandettini, P.; and Lin, C. 2012. Does feature selection improve classification accuracy? impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60(1):59-70.
8. Dai, Z.; Yan, C.; Wang, Z.; Wang, J.; Xia, M.; Li, K.; and He, Y. 2012. Discriminative analysis of early alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (m3). Neuroimage 59(3):2187-2195.
9. Donoho, D., and Johnstone, I. 1995. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association 90(432):1200-1224.
10. Friedman, J.; Hastie, T.; Hofling, H.; and Tibshirani, R. 2007. Pathwise coordinate optimization. The Annals of Applied Statistics 1(2):302-332.
11. Fujishige, S.; Hayashi, T.; and Isotani, S. 2006. The minimum-norm-point algorithm applied to submodular function minimization and linear programming. Technical Report RIMS-1571, Research Institute for Mathematical Sciences, Kyoto University.
12. Fujishige, S. 2005. Submodular Functions and Optimization. Elsevier, 2 edition.
13. Gallo, G.; Grigoriadis, M.; and Tarja, R. 1989. A fast parametric maximum flow algorithm and applications. SIAM Journal of Computing 18(1):30-55.
14. Gill, P.; Murray, W.; and Saunders, M. 1999. User's guide for SNOPT 5.3: A fortran package for large-scale nonlinear programming. Technical report, University of California, San Diego.
15. Goldfarb, D., and Yin, W. 2009. Parametric maximum flow algorithms for fast total variation minimization. SIAM Journal on Scientific Computing 31(5):3712-3743.
16. Grant, M.; Boyd, S.; and Ye, Y. 2008. Cvx: Matlab software for disciplined convex programming.
17. Huang, J.; Zhang, T.; and Metaxas, D. 2011. Learning with structured sparsity. Journal of Machine Learning Research 12:3371-3412.
18. Kolmogorov, V., and Zabin, R. 2004. What energy functions can be minimized via graph cuts? IEEE Trans. on Pattern Analysis and Machine Intelligence 26(2):147-159.
19. Liu, J.; Ji, S.; and Ye, J. 2009. SLEP: Sparse learning with efficient projections.
20. Liu, J.; Yuan, L.; and Ye, J. 2010. An efficient algorithm for a class of fused Lasso problems. In Proc. of the 16th ACM SIGKDD Int'l Conf on Knowledge Discovery and Data Mining, 323-332.
21. Mairal, J.; Jenatton, R.; Obozinski, G.; and Bach, F. 2011. Convex and network flow optimization for structured sparsity. Journal of Machine Learning Research 12:2681-2720.
22. Nagano, K., and Aihara, K. 2012. Equivalent of convex minimization problems over base polytopes. Japan Journal of Industrial and Applied Mathematics 29:519-534.
23. Nagano, K., and Kawahara, Y. 2013. Structured convex optimization under submodular constraints. In in Proc. of the 29th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI'13), 459-468.
24. Nemirovsky, A., and Yudin, D. 1983. Problem Complexity and Method Efficiency in Optimization. Wiley-Interscience Series in Discrete Mathematics. John Wiley & Sons.
25. Nesterov, Y. 2004. Introductory Lectures on Convex Optimization: A Basic Course. Springer.
26. Orlin, J. 2009. A faster strongly polynomial time algorithm for submodular function minimization. Mathematical Programming 118:237-251.
27. Polëiìak, B. 1987. Introduction to Optimization. Optimization Software, Publications Division (New York).
28. Tibshirani, R., and Taylor, J. 2011. The solution path of the generalized lasso. Annals of Statistics 39(3):1335-1371.
29. Tibshirani, R.; Saunders, M.; Rosset, S.; Zhu, J.; and Knight, K. 2005. Sparsity and smoothness via the fused Lasso. Journal of the Royal Statistical Society: Series B 67(1):91-108.
30. Tibshirani, R. 1996. Regression shrinkage and selection via the Lasso. Journal of Royal Statistical Society B 58(1):267-288.
31. Zhou, J.; Yuan, L.; Liu, J.; and Ye, J. 2011. A multi-task learning formulation for predicting disease progression. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 814-822. ACM.