Distributed Power Mean Fusion

Taylor, CN; Bishop, AN

Distributed Power Mean Fusion

Taylor, CN Bishop, AN

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: FUSION 2019 - 22nd International Conference on Information Fusion, 2020
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Taylor, CN
dc.contributor.author	Bishop, AN
dc.date	2019-07-02
dc.date.accessioned	2021-05-20T01:15:35Z
dc.date.available	2021-05-20T01:15:35Z
dc.date.issued	2020
dc.identifier.citation	FUSION 2019 - 22nd International Conference on Information Fusion, 2020
dc.identifier.isbn	9780996452786
dc.identifier.uri	http://hdl.handle.net/10453/148995
dc.description.abstract	© 2019 ISIF-International Society of Information Fusion. The problem of distributed information (data) fusion is considered in this paper. Using a new class of functionals to perform data fusion-the power mean-information fusion can be performed in a dependency/correlation-agnostic manner while achieving better fusion results than many classical fusion techniques (i.e. linear or log-linear pooling). In addition, power mean techniques converge much more rapidly (even in finite time) in distributed network settings. Computing the power mean on generic probability distribution functions is specifically addressed in this paper, including a distributed protocol and specific steps for ensuring distributed convergence to the global power mean. Results demonstrate: 1). convergence to the global power mean even in a distributed setting, 2). the ability for certain power means to converge in finite time, and 3). the improved fusion results achieved by using the power mean over more traditional fusion techniques, even with relatively complex distributions.
dc.language	en
dc.publisher	IEEE
dc.relation	Defence Science and Technology Group of the Department of Defence
dc.relation	Defence Science and Technology Group of the Department of Defence
dc.relation.ispartof	FUSION 2019 - 22nd International Conference on Information Fusion
dc.relation.ispartof	International Conference on Information Fusion
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Distributed Power Mean Fusion
dc.type	Conference Proceeding
utslib.location.activity	Ottawa, ON, Canada
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-20T01:15:34Z
pubs.finish-date	2019-07-05
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-07-02
dc.location	Piscataway, USA

Abstract:

© 2019 ISIF-International Society of Information Fusion. The problem of distributed information (data) fusion is considered in this paper. Using a new class of functionals to perform data fusion-the power mean-information fusion can be performed in a dependency/correlation-agnostic manner while achieving better fusion results than many classical fusion techniques (i.e. linear or log-linear pooling). In addition, power mean techniques converge much more rapidly (even in finite time) in distributed network settings. Computing the power mean on generic probability distribution functions is specifically addressed in this paper, including a distributed protocol and specific steps for ensuring distributed convergence to the global power mean. Results demonstrate: 1). convergence to the global power mean even in a distributed setting, 2). the ability for certain power means to converge in finite time, and 3). the improved fusion results achieved by using the power mean over more traditional fusion techniques, even with relatively complex distributions.

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http://hdl.handle.net/10453/148995