TY - GEN
T1 - Non-orthogonal constrained independent vector analysis
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
AU - Bhinge, Suchita
AU - Long, Qunfang
AU - Levin-Schwartz, Yuri
AU - Boukouvalas, Zois
AU - Calhoun, Vince D.
AU - Adali, Tulay
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many practical applications, important prior information about the data exists and incorporating this information into the IVA model is expected to yield improved separation performance. In this paper, we propose a general formulation for non-orthogonal constrained IVA (C-IVA) framework that can incorporate prior information about either the sources or the mixing coefficients into the IVA cost function. A powerful decoupling method is the major enabling factor in this task. We demonstrate the improved performance of C-IVA over the unconstrained IVA model using both simulated as well as real medical imaging data.
AB - The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many practical applications, important prior information about the data exists and incorporating this information into the IVA model is expected to yield improved separation performance. In this paper, we propose a general formulation for non-orthogonal constrained IVA (C-IVA) framework that can incorporate prior information about either the sources or the mixing coefficients into the IVA cost function. A powerful decoupling method is the major enabling factor in this task. We demonstrate the improved performance of C-IVA over the unconstrained IVA model using both simulated as well as real medical imaging data.
KW - Constrained optimization
KW - data fusion
KW - independent vector analysis
KW - multivariate
UR - http://www.scopus.com/inward/record.url?scp=85020189392&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952640
DO - 10.1109/ICASSP.2017.7952640
M3 - Conference contribution
AN - SCOPUS:85020189392
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2666
EP - 2670
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 March 2017 through 9 March 2017
ER -