Functional Multiple-Set Canonical Correlation Analysis

Psychometrika

Volumn 77, Issue 1, 2012, Pages 48-64

  Hwang, Heungsun ^a   Jung, Kwanghee ^a   Takane, Yoshio ^a   Woodward, Todd S ^b  

^a MCGILL UNIVERSITY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

functional canonical correlation analysis; functional data; functional magnetic resonance imaging data; multiple set canonical correlation analysis

Indexed keywords

EID: 84155189029     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11336-011-9234-4     Document Type: Article

References (36)

1. Almansa, J., & Delicado, P. (2009). Analysing musical performance through functional data analysis: rhythmic structure in Schumann's Traumerei. Connection Science, 21, 207-225.
2. Cairo, T. A., Woodward, T. S., & Ngan, E. T. C. (2006). Decreased encoding efficiency in schizophrenia. Biological Psychiatry, 59(8), 740-746.
3. Carroll, J. D. (1968). A generalization of canonical correlation analysis to three or more sets of variables. Proceedings of the 76th Annual Convention of the American Psychological Association, 3, 227-228.
4. Correa, N. M., Li, Y., Adali, T., & Calhoun, V. D. (2009). Fusion of fMRI, sMRI, and EEG data using canonical correlation analysis. In Proceedings of IEEE international conference on acoustics, speech, and signal processing (pp. 385-388). Washington: IEEE.
5. Correa, N. M., Eichele, T., Adali, T., Li, Y., & Calhoun, V. D. (2010). Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI. Neuroimage, 50, 1438-1445.
6. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, 102, 9673-9678.
7. Gifi, A. (1990). Nonlinear multivariate analysis. Chichester: Wilye.
8. Goutte, C., Nielsen, F. A., & Hansen, L. K. (2000). Modeling the hemodynamic response in fMRI using smooth FIR filters. IEEE Transactions on Medical Imaging, 19, 1188-1201.
9. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). New York: Springer.
10. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12, 55-67.
11. Horst, P. (1961). Generalized canonical correlations and their applications to experimental data. Journal of Clinical Psychology, 17, 331-347.
12. Hunter, M. A., & Takane, Y. (2002). Constrained principal component analysis: various applications. Journal of Educational and Behavioral Statistics, 27, 105-145.
13. Hwang, H., & Takane, Y. (2002). Generalized constrained multiple correspondence analysis. Psychometrika, 67, 211-224.
14. Jackson, I., & Sirois, S. (2009). Infant cognition: going full factorial with pupil dilation. Developmental Science, 12, 670-679.
15. Lee, S., Bresch, E., & Narayanan, S. (2006). An exploratory study of emotional speech production using functional data analysis techniques. In H. C. Yehia, D. Demolin, & R. Laboissiere (Eds.), Proceedings of the 7th international seminar on speech production (pp. 525-532). Ubatuba: CEFALA.
16. Leurgans, S. E., Moyeed, R. A., & Silverman, B. W. (1993). Canonical correlation analysis when data are curves. Journal of the Royal Statistical Society B, 55, 725-740.
17. Mattar, A. A. G., & Ostry, D. J. (2010). Generalization of dynamics learning across changes in movement amplitude. The Journal of Neurophysiology, 104, 426-438.
18. Meredith, W. (1964). Rotation to achieve factorial invariance. Psychometrika, 29, 187-206.
19. Metzak, P. D., Feredoes, E., Takane, Y., Wang, L., Weinstein, S., Cairo, T., Ngan, E. T. C., & Woodward, T. S. (2010). Constrained principal component analysis reveals functionally connected load-dependent networks involved in multiple stages of working memory. Human Brain Mapping, 32, 856-871. doi: 10. 1002/hbm. 21072.
20. Metzak, P. D., Riley, J., Wang, L., Whitman, J. C., Ngan, E. T. C., & Woodward, T. S. (2011). Decreased efficiency of task-positive and task-negative networks during working memory in schizophrenia. Schizophrenia Bulletin. Advance online publication. doi: 10. 1093/schbul/sbq154.
21. Olshen, R. A., Biden, E. N., Wyatt, M. P., & Sutherland, D. H. (1989). Gait analysis and the bootstrap. Annals of Statistics, 17, 1419-1440.
22. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1999). Numerical recipes in C. The art of scientific computing (2nd ed.). Cambridge: Cambridge University Press.
23. Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis (2nd ed.). New York: Springer.
24. Ramsay, J. O., Hooker, G., & Graves, S. (2009). Functional data analysis with R and Matlab. New York: Springer.
25. Rice, J. A., & Silverman, B. W. (1991). Estimating the mean and covariance structure non-parametrically when the data are curves. The Journal of the Royal Statistical Society B, 53, 233-243.
26. Takane, Y., & Hunter, M. A. (2001). Constrained principal component analysis: a comprehensive theory. Applicable Algebra in Engineering, Communication, and Computing, 12, 391-419.
27. Takane, Y., & Hwang, H. (2002). Generalized constrained canonical correlation analysis. Multivariate Behavioral Research, 37, 163-195.
28. Takane, Y., & Oshima-Takane, Y. (2002). Nonlinear generalized canonical correlation analysis by neural network models. In S. Nishisato, Y. Baba, H. Bozdogan, & K. Kanefuji (Eds.), Measurement and multivariate analysis (pp. 183-190). Tokyo: Springer.
29. Takane, Y., & Shibayama, T. (1991). Principal component analysis with external information on both subjects and variables. Psychometrika, 56, 97-120.
30. Takane, Y., Hwang, H., & Abdi, H. (2008). Regularized multiple-set canonical correlation analysis. Psychometrika, 73, 753-775.
31. Tian, T. S. (2010). Functional data analysis in brain imaging studies. Frontiers in Quantitative Psychology and Measurement, 1, 1-11.
32. Vines, B. W., Nuzzo, R. L., & Levitin, D. J. (2005). Quantifying and analyzing musical dynamics: differential calculus, physics and functional data techniques. Music Perception, 23(2), 137-152.
33. Vines, B. W., Krumhansl, C. L., Wanderley, M. M., & Levitin, D. J. (2006). Cross-modal interactions in the perception of musical performance. Cognition, 101, 80-113.
34. Vinod, H. D. (1976). Canonical ridge and econometrics of joint production. Journal of Econometrics, 4, 47-166.
35. Wedel, M., & Kamakura, W. A. (1998). Market segmentation: conceptual and methodological foundations. Boston: Kluwer Academic.
36. Yanai, H. (1998). Generalized canonical correlation analysis with linear constraints. In C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H.-H. Bock, & Y. Baba (Eds.), Data science, classification, and related methods (pp. 539-546). Tokyo: Springer.