Decentralized independent vector analysis

Nikolas P. Wojtalewicz, Rogers F. Silva, Vince Daniel Calhoun, Anand D. Sarwate, Sergey M. Plis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages826-830
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - Jun 16 2017
Externally publishedYes
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

    Fingerprint

Keywords

  • blind source separation
  • decentralized data
  • distributed signal processing
  • IVA

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Wojtalewicz, N. P., Silva, R. F., Calhoun, V. D., Sarwate, A. D., & Plis, S. M. (2017). Decentralized independent vector analysis. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 826-830). [7952271] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952271