Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators

Medical Image Analysis

Volumn 16, Issue 7, 2012, Pages 1359-1370

  Fritsch, Virgile ^a,c   Varoquaux, Gaël ^a,b,c   Thyreau, Benjamin ^c   Poline, Jean Baptiste ^a,c   Thirion, Bertrand ^a,c  

^a INRIA SACLAY   (France)

^b INSERM   (France)

^c DIF   (France)

Author keywords

High dimension; Minimum covariance determinant; Neuroimaging; Outlier detection; Robust estimation

Indexed keywords

DATA SCREENING; DATA SETS; EXTENSIVE SIMULATIONS; FUNCTIONAL FEATURES; GROUND TRUTH; GROUP STUDY; HIGH-DIMENSION; HIGH-DIMENSIONAL; HOMOGENEOUS SAMPLES; MINIMUM COVARIANCE DETERMINANT; MULTIVARIATE MODELS; OUTLIER DETECTION; OUTLIER DETECTION PROCEDURE; REGULARIZATION SCHEMES; ROBUST ESTIMATION; SMALL SAMPLE SIZE; STATE-OF-THE-ART PROCEDURES; STATISTICAL CONTROL;

ESTIMATION; FUNCTIONAL NEUROIMAGING; MEDICAL IMAGING; MULTIVARIANT ANALYSIS; NEUROIMAGING;

STATISTICS;

ARTICLE; COVARIANCE; DIAGNOSIS RELATED GROUP; DIAGNOSTIC IMAGING; FUNCTIONAL NEUROIMAGING; KERNEL METHOD; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL;

ALGORITHMS; ARTIFACTS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; NEUROIMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84866444366     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2012.05.002     Document Type: Article

References (40)

1. Chen Y., Wiesel A., Eldar Y., Hero A. Shrinkage algorithms for MMSE covariance estimation. I EEE Transactions on Signal Processing 2010, 58:5016-5029.
2. Daszykowski M., Kaczmarek K., Heyden Y.V., Walczak B. Robust statistics in data analysis - a review: Basic concepts. Chemometrics and Intelligent Laboratory Systems 2007, 85:203-219.
3. Dave R., Krishnapuram R. Robust clustering methods: a unified view. IEEE Transactions on Fuzzy Systems 1997, 5:270-293.
4. Diaconis P., Freedman D. Asymptotics of graphical projections. The Annals of Statistics 1984, 12:793-815.
5. Falangola M., Jensen J., Babb J., Hu C., Castellanos F., Di Martino A., Ferris S., Helpern J. Age-related non-Gaussian diffusion patterns in the prefrontal brain. Journal of Magnetic Resonance Imaging 2008, 28:1345-1350.
6. Friedman, J., Hastie, T., Tibshirani, R., 2007. Sparse inverse covariance estimation with the lasso. ArXiv e-prints.
7. Garcia-Escudero L., Gordaliza A. Robustness properties of K-means and trimmed K-means. Journal of the American Statistical Association 1999, 94:956-969.
8. Gardner A., Krieger A., Vachtsevanos G., Litt B. One-class novelty detection for seizure analysis from intracranial EEG. The Journal of Machine Learning Research 2006, 7:1025-1044.
9. Hamilton W.C. The revolution in crystallography. Science 1970, 169:133-141.
10. Hanley J.A., McNeil B.J. The meaning and use of the area under a receiver operating (ROC) curve characteristic. Radiology 1982, 143:29-36.
11. Hardin J., Rocke D.M. The distribution of robust distances. Journal of Computational and Graphical Statistics 2005, 14:928-946.
12. Huber P.J. Robust Statistics 2005, John Wiley & Sons, Inc., p. 149 (Chapter 7).
13. Hubert M., Engelen S. Robust PCA and classification in biosciences. Bioinformatics 2004, 20:1728-1736.
14. Johnson W., Lindenstrauss J., Schechtman G. Extensions of Lipschitz maps into Banach spaces. Israel Journal of Mathematics 1986, 54:129-138.
15. Joshi, S., Bowman, I., Toga, A., Van Horn, J., 2011. Brain pattern analysis of cortical valued distributions, in: Proc IEEE Int Symp Biomed Imaging, pp. 1117-1120.
16. Kherif F., Flandin G., Ciuciu P., Benali H., Simon O., Poline J.B. Model based spatial and temporal similarity measures between series of functional magnetic resonance images. Medical Image Computing and Computer-assisted Intervention 2002, 509-516.
17. Ledoit O., Wolf M. A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 2004, 88:365-411.
18. Mériaux, S., Roche, A., Thirion, B., Dehaene-Lambertz, G., 2006. Robust statistics for nonparametric group analysis in fMRI. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006, pp. 936-939.
19. Mourao-Miranda J., Hardoon D.R., Hahn T., Marquand A.F., Williams S.C., Shawe-Taylor J., Brammer M. Patient classification as an outlier detection problem: an application of the one-class support vector machine. NeuroImage 2011, 58:793-804.
20. Najman L., Schmitt M. Watershed of a continuous function. Signal Processing 1994, 38:99-112.
21. Pea D., Prieto F.J. Multivariate outlier detection and robust covariance matrix estimation. Technometrics 2001, 43:286-310.
22. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay É. Scikit-learn: machine learning in python. Journal of Machine Learning Research 2011.
23. Penny W.D., Kilner J., Blankenburg F. Robust bayesian general linear models. NeuroImage 2007, 36:661-671.
24. Pinel P., Dehaene S., Rivière D., LeBihan D. Modulation of parietal activation by semantic distance in a number comparison task. NeuroImage 2001, 14:1013-1026.
25. Rousseeuw P.J. Least median of squares regression. ournal of the American Statistical Association 1984, 79:871-880.
26. Rousseeuw P.J., Leroy A.M. Robust Regression and Outlier Detection 2005, John Wiley & Sons, Inc., pp. 4-5 (Chapter 1).
27. Rousseeuw P.J., Van Driessen K. A fast algorithm for the minimum covariance determinant estimator. Technometrics 1999, 41:212-223.
28. Roux, N.L., Bach, F., 2011. Local component analysis. CoRR abs/1109.0093.
29. Schölkopf B., Platt J.C., Shawe-Taylor J.C., Smola A.J., Williamson R.C. Estimating the support of a high-dimensional distribution. Neural Computation 2001, 13:1443-1471.
30. Schumann G., Loth E., Banaschewski T., Barbot A., Barker G., Büchel C., Conrod P., Dalley J., Flor H., Gallinat J., Garavan H., Heinz A., Itterman B., Lathrop M., Mallik C., Mann K., Martinot J.L., Paus T., Poline J.B., Robbins T., Rietschel M., Reed L., Smolka M., Spanagel R., Speiser C., Stephens D., Ströhle A., Struve M. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Molecular Psychiatry 2010, 15:1128-1139.
31. Seabold, S., Perktold, J., 2010. Statsmodels: econometric and statistical modeling with python. In: van der Walt, S., Millman, J. (Eds.), Proceedings of the 9th Python in Science Conference, pp. 57-61.
32. Segata N., Blanzieri E. Fast and scalable local kernel machines. The Journal of Machine Learning Research 2009, 11:1883-1926.
33. Shapiro S.S., Wilk M.B. An analysis of variance test for normality (complete samples). Biometrika 1965, 52:591-611.
34. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 1994, 58:267-288.
35. Upadhyaya A., Rieu J., Glazier J., Sawada Y. Anomalous diffusion and non-Gaussian velocity distribution of hydra cells in cellular aggregates. Physica A: Statistical Mechanics and its Applications 2001, 293:549-558.
36. Varoquaux G., Sadaghiani S., Pinel P., Kleinschmidt A., Poline J., Thirion B. A group model for stable multi-subject ICA on fMRI datasets. NeuroImage 2010, 51:288-299.
37. Wang, J., Saligrama, V., Castañón, D.A., 2011. Structural similarity and distance in learning. ArXiv e-prints.
38. Wetzel S.G., Johnson G., Tan A.G.S., Cha S., Knopp E.A., Lee V.S., Thomasson D., Rofsky N.M. Three-dimensional, t1-weighted gradient-echo imaging of the brain with a volumetric interpolated examination. American Journal of Neuroradiology 2002, 23:995-1002.
39. Woolrich M. Robust group analysis using outlier inference. NeuroImage 2008, 41:286-301.
40. Zweig M., Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 1993, 39:561-577.