Browse > Article
http://dx.doi.org/10.5351/KJAS.2021.34.2.127

Robust group independent component analysis  

Kim, Hyunsung (Department of Statistics, Chung-Ang University)
Li, XiongZhu (Department of Statistics, Chung-Ang University)
Lim, Yaeji (Department of Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.2, 2021 , pp. 127-139 More about this Journal
Abstract
Independent Component Analysis is a popular statistical method to separate independent signals from the mixed data, and Group Independent Component Analysis is an its multi-subject extension of Independent Component Analysis. It has been applied Functional Magnetic Resonance Imaging data and provides promising results. However, classical Group Independent Component Analysis works poorly when outliers exist on data which is frequently occurred in Magnetic Resonance Imaging scanning. In this study, we propose a robust version of the Group Independent Component Analysis based on ROBPCA. Through the numerical studies, we compare proposed method to the conventional method, and verify the robustness of the proposed method.
Keywords
functional magnetic resonance imaging(fMRI); independent component analysis; group independent component analysis; robustness; ROBPCA;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Hubert M, Rousseeuw PJ, and Vanden Branden K (2005). ROBPCA:A new approach to robust principal component analysis, American Statistical Association and the American Society for Quality, 47.
2 Hyvarinen A (1999). Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, 10, 626-634.   DOI
3 Hyvarinen A (2013). Independent component analysis: recent advances, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371, 20110534.   DOI
4 Langlois D, Chartier S, and Gosselin D (2010). An introduction to independent component analysis: infoMax and fastICA algorithms, Tutorials in Quantitative Methods for Psychology, 6, 31-38.   DOI
5 McKeown MJ, Jung TP, Makeig S, Brown G, Kindermann SS, Lee TW, and Sejnowski TJ (1998). Spatially independent activity patterns in functional MRI data during the stroop color-naming task. In Proceedings of the National Academy of Sciences, 95, 803-810.   DOI
6 Rousseeuw PJ (1984). Least median of squares regression, Journal of the American Statistical Association, 89, 871-880.   DOI
7 Rousseeuw PJ and Driessen KV (1999). A fast algorithm for the minimum covariance determinant estimator, Technometrics, 41, 212-223.   DOI
8 Todorov V (2009). Rrcov: scalable robust estimators with high breakdown point, r package version 0.5-03. Available from: http://CRAN.R-project.org/package=rrcov.
9 Calhoun VD, Adali T, Pearlson GD, and Pekar JJ (2001). A method for making group inferences from functional MRI data using independent component analysis, Human Brain Mapping, 14, 140-151.   DOI
10 Hyvarinen A and Oja E (2000). Independent component analysis: algorithms and applications, Journal of Neural Networks, 13, 411-430.   DOI
11 De Klerk J (2015). Time series outlier detection using the trajectory matrix in singular spectrum analysis with outlier maps and ROBPCA, South African Statistical Journal, 49, 61-76.
12 Bai P, Shen H, Huang X, and Truong Y (2008). A supervised singular value decomposition for independent component analysis for fMRI, Statistica Sinica, 18, 1233-1252.
13 McKeown MJ and Sejnowski TJ (1998). Independent component analysis of FMRI data: examining the assumptions, Human Brain Mapping 6, 368-372.   DOI
14 Meinecke FC, Harmeling S, and Muller KR (2004). Robust ICA for super-Gaussian sources, In International Conference on Independent Component Analysis and Signal Separation, 17-224, Springer, Berlin, Heidelberg.
15 Pruim RH, Mennes M, van Rooij D, Llera A, Buitelaar JK, and Beckmann CF (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data, Neuroimage, 112, 267-277.   DOI
16 Rachakonda S, Egolf E, Correa N, and Calhoun V (2007). Group ICA of fMRI toolbox (GIFT) manual. Available from: http://www.nitrc.org/docman/view.php/55/295.
17 Biswal BB and Ulmer JL (1999). Blind source separation of multiple signal sources of fMRI data sets using independent component analysis, Journal of Computer Assisted Tomography, 23, 265-271.   DOI
18 Bordier C, Dojat M, and de Micheaux PL (2011). Temporal and spatial independent component analysis for fMRI data sets embedded in the AnalyzeFMRI r package, Journal of Statistical Software, 44, 1-24.
19 Bulut H and Oner Y (2017). The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses, Journal of Applied Statistics, 44, 2936- 2948.   DOI
20 Comon P (1994). Independent component analysis, a new concept?, Signal Processing, 36, 287-314.   DOI