Abstract
In this work, we propose a structured approach to compare common and distinct features of two multidimensional datasets using a combination of canonical correlation analysis (CCA) and independent component analysis (TCA). We develop formulations of information theoretic criteria to determine the dimension of the subspaces for common and distinct features of the two datasets. We apply the proposed method to a simulated dataset to demonstrate that it improves the estimation of both common and distinct features when compared to performing ICA on the concatenation of two datasets. We also apply the method to compare brain activation in functional magnetic resonance imaging (fMRI) data acquired during a simulated driving experiment and observe distinctions between the driving and watching conditions revealed in relevant brain function studies.
| Original language | English (US) |
|---|---|
| Title of host publication | Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP |
| Pages | 217-222 |
| Number of pages | 6 |
| DOIs | |
| State | Published - 2007 |
| Externally published | Yes |
| Event | 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, Greece Duration: Aug 27 2007 → Aug 29 2007 |
Other
| Other | 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 |
|---|---|
| Country | Greece |
| City | Thessaloniki |
| Period | 8/27/07 → 8/29/07 |
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ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
Cite this
A multivariate model for comparison of two datasets and its application to FMRI analysis. / Li, Yi Ou; Adali, Tülay; Calhoun, Vince Daniel.
Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP. 2007. p. 217-222 4414309.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A multivariate model for comparison of two datasets and its application to FMRI analysis
AU - Li, Yi Ou
AU - Adali, Tülay
AU - Calhoun, Vince Daniel
PY - 2007
Y1 - 2007
N2 - In this work, we propose a structured approach to compare common and distinct features of two multidimensional datasets using a combination of canonical correlation analysis (CCA) and independent component analysis (TCA). We develop formulations of information theoretic criteria to determine the dimension of the subspaces for common and distinct features of the two datasets. We apply the proposed method to a simulated dataset to demonstrate that it improves the estimation of both common and distinct features when compared to performing ICA on the concatenation of two datasets. We also apply the method to compare brain activation in functional magnetic resonance imaging (fMRI) data acquired during a simulated driving experiment and observe distinctions between the driving and watching conditions revealed in relevant brain function studies.
AB - In this work, we propose a structured approach to compare common and distinct features of two multidimensional datasets using a combination of canonical correlation analysis (CCA) and independent component analysis (TCA). We develop formulations of information theoretic criteria to determine the dimension of the subspaces for common and distinct features of the two datasets. We apply the proposed method to a simulated dataset to demonstrate that it improves the estimation of both common and distinct features when compared to performing ICA on the concatenation of two datasets. We also apply the method to compare brain activation in functional magnetic resonance imaging (fMRI) data acquired during a simulated driving experiment and observe distinctions between the driving and watching conditions revealed in relevant brain function studies.
UR - http://www.scopus.com/inward/record.url?scp=48149102534&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48149102534&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2007.4414309
DO - 10.1109/MLSP.2007.4414309
M3 - Conference contribution
SN - 1424415667
SN - 9781424415663
SP - 217
EP - 222
BT - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
ER -