An outlier map for Support Vector Machine classification

Annals of Applied Statistics

Volumn 3, Issue 4, 2009, Pages 1566-1580

  Debruyne, Michiel ^a  

^a UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

Data visualization; High dimensional data analysis; Robust statistics; Support Vector Machine

Indexed keywords

EID: 77955850624     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/09-AOAS256     Document Type: Article

References (25)

1. Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D. and Levine, A. J. (1999). Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96 6475-6750.
2. Chiaretti, S., Li, X., Gentleman, R., Vitale, A., Vignetti, M., Mandelli, F., Ritz, J. and Foa, R. (2004). Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood 103 2771-2778.
3. Christmann, A. and Steinwart, I. (2004). On robustness properties of convex risk minimization methods for pattern recognition. J. Mach. Learn. Res. 51007-1034.
4. Donoho, D. L. (1982). Breakdown properties of multivariate location estimators. Qualifying paper. Harvard Univ.
5. Furey, T. S., Cristianini, N., Duffy, D., Bednarski, W., Schummer, M. and Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics16 906-914.
6. Jaakkola, T., Diekhans, M. and Haussler, D. (2000). A discriminative framework for detecting remote protein homologies. J. Comput. Biol. 795-114.
7. Guyon, I., Weston, J., Barnhill, S. and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Mach. Learn. 46 389-422.
8. Hubert, M. and Engelen, S. (2004). Robust PCA and classification in biosciences. Bioinformatics 20 1728-1736.
9. Hubert, M., Rousseeuw, P. J. and Vanden Branden, K. (2005). ROBPCA: A new approach to robust principal component analysis. Technometrics 47 64-79.
10. Kadota, K., Tominaga, D., Akiyama, Y. and Takahashi, K. (2003). Detecting outlying samples in microarray data: A critical assessment of the effect of outliers on sample classification. Chem-Bio. Inform. J. 3 30-45.
11. Leslie, C., Eskin, E. and Noble, W. S. (2002). The spectrum kernel: A string kernel for svm protein classification. In Proceedings of the Pacific Symposium on Biocomputing 2002 (R. B. Altman, A. K. Dunker, L. Hunter, K. Lauerdale and T. E. Klein, eds.) 564-575. World Scientific, Hackensack, NJ.
12. Leslie, C., Eskin, E., Weston, J. and Noble, W. S. (2003). Mismatch string kernels for svm protein classification. In Advances in Neural Information Processing Systems (S. Becker, S. Thrun and K. Obermayer, eds.) 151441-1448. MIT Press, Cambridge, MA.
13. Li, L., Darden, T. A., Weinberg, C. R., Levine, A. J. and Pedersen, L. G. (2001). Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. Comb. Chem. High Throughput Screen. 4 727-739.
14. Liao, L. and Noble, W. S. (2002). Combining pairwise sequence similarity and support vector machines for remote protein homology detection. In Proceedings of the Sixth International Conference on Computational Molecular Biology (T. Lengauer, ed.) 225-232. ACM Press, New York.
15. Malossini, A., Blanzieri, E. and Ng, R. T. (2006). Detecting potential labeling errors in microarrays by data perturbation. Bioinformatics 22 2114-2121.
16. Maronna, R. and Yohai, V. (1995). The behavior of the Stahel-Donoho robust multivariate estimator. J. Amer. Statist. Assoc. 90 330-341.
17. Pochet, N., De Smet, F., Suykens, J. A. K. and De Moor, B. (2004). Systematic benchmarking of microarray data classification: Assessing the role of nonlinearity and dimensionality reduction. Bioinformatics 20 3185-3195.
18. Pollack, J. D., Li, Q. and Pearl, D. K. (2005). Taxonomic utility of a phylogenetic analysis of phosphoglycerate kinase proteins of Archaea, Bacteria and Eukaryota: Insights by Bayesian analyses. Mol. Phylogenet. Evol. 35 420-430.
19. Rousseeuw, P. J. and Van Zomeren, B. C. (1990). Unmasking multivariate outliers and leverage points. J. Amer. Statist. Assoc. 85 633-639.
20. Saigo, H., Vert, J., Ueda, N. and Akutsul, T. (2004). Protein homology detection using string alignment kernels. Bioinformatics 20 1682-1689.
21. Schölkopf, B. and Smola, A. (2002). Learning with Kernels. MIT Press, Cambridge, MA.
22. Stahel, W. A. (1981). Robuste Schätzungen: Infinitesimale optimalität und schätzungen von kovarianzmatrizen. Ph.D. thesis, ETH Zürich.
23. Steinwart, I. and Christmann, A. (2008). Support Vector Machines. Springer, New York.
24. Vapnik, V. (1998). Statistical Learning Theory. Wiley, New York.
25. West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Marks, J. R. and Nevins, J. R. (2001). Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Natl. Acad. Sci. 98 11462-11467.