Sparsity control for robust principal component analysis

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn , Issue , 2010, Pages 1925-1929

  Mateos, Gonzalo ^a   Giannakis, Georgios B ^a  

^a University of Minnesota   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPONENT ANALYSIS; COMPRESSIVE SAMPLING; CONVEX RELAXATION; DATA MODELS; FEATURE SPACE; HIGH DIMENSIONAL DATA; MATRIX; NOVEL ALGORITHM; NUMERICAL TESTS; PREFERENCE MEASUREMENT; PRINCIPAL COMPONENTS; REGULARIZATION PARAMETERS; ROBUST ESTIMATORS; ROBUST PCA; ROBUST PRINCIPAL COMPONENT ANALYSIS; ROBUSTIFICATION; VARIABLE SELECTION; VIDEO SURVEILLANCE;

ALGORITHMS; BIOINFORMATICS; COMPUTER APPLICATIONS; COMPUTER CONTROL SYSTEMS; COMPUTER VISION; DATA REDUCTION; ESTIMATION; OBJECT RECOGNITION; RELAXATION PROCESSES; ROBUST CONTROL; SECURITY SYSTEMS; STATISTICS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79957992016     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2010.5757875     Document Type: Conference Paper

References (19)

1. N. A. Campbell, "Robust procedures in multivariate analysis I: Robust covariance estimation," Applied Stat., vol. 29, pp. 231-237, 1980.
2. E. J. Candes, X. Li, Y. Ma, and J. Wright, "Robust principal component analysis?," 2009 (submitted).
3. F. de la Torre and M. J. Black, "A framework for robust subspace learning," Int. Jrnl. of Computer Vision, vol. 54, pp. 183-209, 2003.
4. J. J. Fuchs, "An inverse problem approach to robust regression," in Proc. of ICASSP, Phoeniz, AZ, Mar. 1999.
5. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY: Springer, 2009.
6. P. J. Huber and E. Ronchetti, Robust Statistics, New York: Wiley, 2009.
7. I. T. Jolliffe, Principal Component Analysis, 2nd ed. New York, NY: Springer, 2002.
8. V. Kekatos and G. B. Giannakis, "Robust layered sensing: From sparse signals to sparse residuals," in Proc. of 44th Asilomar Conf., Pacific Grove, CA, Nov. 2010; http://arxiv.org/abs/1011.0450
9. H. Kim, S. Lee, X. Ma, and C. Wang, "Higher-order PCA for anomaly detection in large-scale networks," in Proc. of CAMSAP, Aruba, Dutch Antilles, Dec. 2009.
10. J. Mairal, J. Bach, J. Ponce, and G. Sapiro, "Online learning for matrix factorization and sparse coding," Jrnl. of Machine Learning Research, vol. 11, pp. 19-60, Jan. 2010.
11. G. Mateos and G. B. Giannakis, "Sparsity control for robust principal component analysis," IEEE Trans. Sig. Proc., 2010 (submitted).
12. P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection, New York, NY: Wiley, 1987.
13. B. Schlkopf, A. Smola, and K.-R. Mller, "Kernel principal component analysis," in Artificial Neural Networks: Lec. Notes in Computer Science, vol. 1327, pp. 583-588, Berlin: Springer, 1997.
14. J. Tropp, "Just relax: Convex programming methods for identifying sparse signals," IEEE Trans. Info. Theory, vol. 52, no. 3, pp. 1030-1051, Mar. 2006.
15. L. Xu and A. L. Yuille, "Robust principal component analysis by self-organizing rules based on statistical physics approach," IEEE Trans. Neural Nets., vol. 6, no. 1, pp. 131-143, Jan. 1995.
16. B. Yang, "Projection approximation subspace tracking," IEEE Trans. Sig. Proc., vol. 43, no. 1, pp. 95-107, Jan. 1995.
17. M. Yuan and Y. Lin, "Model selection and estimation in regression with grouped variables," Jrnl. of the Royal Stat. Society, Series B, vol. 68, no. 1, pp. 4967, 2006.
18. H. Zou, "The adaptive Lasso and its oracle properties," Jrnl. of the American Stat. Assoc., vol. 101, no. 476, pp. 1418-1429, Dec. 2006.
19. H. Zou, T. Hastie, and R. Tibshirani, "Sparse principal component analysis," Jrnl. of Comp. and Graphical Statistics, vol. 15, no. 2, pp. 265-286, 2006.