TY - GEN
T1 - Decentralized independent vector analysis
AU - Wojtalewicz, Nikolas P.
AU - Silva, Rogers F.
AU - Calhoun, Vince D.
AU - Sarwate, Anand D.
AU - Plis, Sergey M.
N1 - Funding Information:
This work was sponsored in part by NSF award CCF-1453432, NIH award 1R01DA040487-01A1, and DARPA and SSC Pacific under contract No. N66001-15-C-4070.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Independent vector analysis (IVA) is an approach for joint blind source separation of several data sets that learns simultaneous unmixing transforms for each set. It assumes corresponding sources from different data sets to be statistically dependent. One of the main advantages is IVA's ability to retain subject-specific differences while simplifying comparison across subjects as the resulting components have the same order. The latter is an instrumental property for enabling collaboration between remote sites without sharing their data, which may be required because of ethical, privacy or efficiency concerns. This paper proposes a new decentralized algorithm for IVA that exploits the structure of the objective function. A centralized aggregator coordinates IVA algorithms at multiple sites using message passing, parallelizing the computation and limiting the amount of communication. Thus, the algorithm enables a plausibly private collaboration across multiple sites. Besides enabling analysis of decentralized data, our approach improves the running time of IVA when used locally.
AB - Independent vector analysis (IVA) is an approach for joint blind source separation of several data sets that learns simultaneous unmixing transforms for each set. It assumes corresponding sources from different data sets to be statistically dependent. One of the main advantages is IVA's ability to retain subject-specific differences while simplifying comparison across subjects as the resulting components have the same order. The latter is an instrumental property for enabling collaboration between remote sites without sharing their data, which may be required because of ethical, privacy or efficiency concerns. This paper proposes a new decentralized algorithm for IVA that exploits the structure of the objective function. A centralized aggregator coordinates IVA algorithms at multiple sites using message passing, parallelizing the computation and limiting the amount of communication. Thus, the algorithm enables a plausibly private collaboration across multiple sites. Besides enabling analysis of decentralized data, our approach improves the running time of IVA when used locally.
KW - IVA
KW - blind source separation
KW - decentralized data
KW - distributed signal processing
UR - http://www.scopus.com/inward/record.url?scp=85023745633&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023745633&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952271
DO - 10.1109/ICASSP.2017.7952271
M3 - Conference contribution
AN - SCOPUS:85023745633
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 826
EP - 830
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -