TY - GEN
T1 - Consecutive Independence and Correlation Transform for Multimodal Fusion
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
AU - Akhonda, Mohammad A.B.S.
AU - Levin-Schwartz, Yuri
AU - Bhinge, Suchita
AU - Calhoun, Vince D.
AU - Adali, Tulay
N1 - Funding Information:
This work was supported in part by NSF-CCF 1618551 and NIH-NIBIB R01 EB 005846.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Methods based on independent component analysis (ICA) and canonical correlation analysis (CCA) as well as their various extensions have become popular for the fusion of multimodal data as they minimize assumptions about the relationships among multiple datasets. Two important extensions that are widely used, joint ICA (jICA) and parallel ICA (pICA), make a number of simplifying assumptions that might limit their usefulness such as identical mixing matrices for jICA, and the requirement for the same number of components for jICA and pICA. In this paper, we propose a new, flexible hybrid method for fusion based on ICA and CCA, called consecutive independence and correlation transform (C-ICT), which relaxes the main limitations of jICA and pICA. We demonstrate performance advantages of C-ICT both through simulations and application to real medical data collected from schizophrenia patients and healthy controls performing an auditory oddball task (AOD).
AB - Methods based on independent component analysis (ICA) and canonical correlation analysis (CCA) as well as their various extensions have become popular for the fusion of multimodal data as they minimize assumptions about the relationships among multiple datasets. Two important extensions that are widely used, joint ICA (jICA) and parallel ICA (pICA), make a number of simplifying assumptions that might limit their usefulness such as identical mixing matrices for jICA, and the requirement for the same number of components for jICA and pICA. In this paper, we propose a new, flexible hybrid method for fusion based on ICA and CCA, called consecutive independence and correlation transform (C-ICT), which relaxes the main limitations of jICA and pICA. We demonstrate performance advantages of C-ICT both through simulations and application to real medical data collected from schizophrenia patients and healthy controls performing an auditory oddball task (AOD).
KW - Canonical Correlation Analysis
KW - Data Fusion
KW - EEG
KW - FMRI
KW - Independent Component Analysis
UR - http://www.scopus.com/inward/record.url?scp=85054201460&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054201460&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8462031
DO - 10.1109/ICASSP.2018.8462031
M3 - Conference contribution
AN - SCOPUS:85054201460
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2311
EP - 2315
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 April 2018 through 20 April 2018
ER -