Robust relevance vector regression with trimmed likelihood function

IEEE Signal Processing Letters

Volumn 14, Issue 10, 2007, Pages 746-749

  Yang, Biao ^a   Zhang, Zengke ^a   Sun, Zhengshun ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Least trimmed square (LTS); Relevance vector machine (RVM); Robust regression

Indexed keywords

MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD; ROBUST CONTROL; SUPPORT VECTOR MACHINES;

LEAST TRIMMED SQUARE (LTS); RELEVEANCE VECTOR MACHINE (RVM);

REGRESSION ANALYSIS;

EID: 34548736528     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2007.898327     Document Type: Article

References (18)

1. M. E. Tipping, "The relevance vector machine," Adv. Neural Inf. Process. Syst., vol. 12, pp. 652-658, Dec. 2000.
2. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, no. 3, pp. 211-244, Sep. 2001.
3. B. Boser, I. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. ACM COLT, Jul. 1992, pp. 144-152.
4. V. N. Vapnik Statistical Learning Theory. New York: Wiley, 1998.
5. A. Agarwal and B. Triggs, "Recovering 3D human pose from monocular images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 1, pp. 44-58, Jan. 2006.
6. F. Liu, J. Z. Zhou, F. P. Qiu, J. J. Yang, and L. Liu, "Nonlinear hydrological time series forecasting based on the relevance vector regression," Lect. Notes. Comput. Sci., vol. 4233, pp. 880-889, Oct. 2006.
7. R. Fujimaki, T. Yairi, and K. Machida, "An anomaly detection method for spacecraft using relevance vector learning," Lect. Notes. Comput. Sci., vol. 3518, pp. 785-790, May 2005.
8. A. C. Faul and M. E. Tipping, "A variational approach to robust regression," Lect. Notes. Comput. Sci., vol. 2130, pp. 95-102, Aug. 2001.
9. M. E. Tipping and N. D. Lawrence, "Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis," Neurocomputing, vol. 69, no. 1-3, pp. 123-141, Dec. 2005.
10. D. J. C. MacKay, "Bayesian methods for backpropagation networks," in Models of Neural Networks III. Berlin, Germany: Springer, 1994, ch. 6, pp. 211-254.
11. R. M. Neal, Bayesian Learning for Neural Networks. Berlin, Germany: Springer, 1996.
12. D. J. C. MacKay, "Bayesian interpolation," Neural Comput., vol. 4, no. 3, pp. 415-447, May 1992.
13. P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection. New York: Wiley, 1987.
14. B. Yang, Z. K. Zhang, and Z. S. Sun, "Robust nonlinear LTS estimation method," J. Tsinghua Univ., vol. 45, no. 10, pp. 1316-1319, Oct. 2005.
15. P. Čížek, "Least trimmed squares in nonlinear regression under dependence," J. Stat. Plan. Infer., vol. 136, no. 11, pp. 3967-3988, Nov. 2006.
16. P. J. Rousseeuw and K. Driessen, "Computing LTS regression for large data sets," Data Min. Knowl. Discov., vol. 12, no. 1, pp. 29-45, Jan. 2006.
17. J. H. Friedman, "Multivariate adaptive regression splines," Annu. Stat., vol. 19, no. 1, pp. 1-67, Mar. 1991.
18. R. R. Meyer and P. M. Roth, "Modified damped least squares: An algorithm for nonlinear estimation," IMA J. Appl. Math., vol. 9, no. 2, pp. 218-233, Apr. 1972.