bibtype	J - Journal Article
ARLID	0477044
utime	20240103214400.1
mtime	20170815235959.9
SCOPUS	85023175924
WOS	000407100700002
DOI	10.1109/TSP.2017.2725226
title (primary) (eng)	Factorized Estimation of Partially Shared Parameters in Diffusion Networks
specification	page_count 11 s. media_type P
serial	ARLID cav_un_epca*0256727 ISSN 1053-587X title IEEE Transactions on Signal Processing volume_id 65 volume 19 (2017) page_num 5153-5163
keyword	Diffusion network
keyword	Diffusion estimation
keyword	Heterogeneous parameters
keyword	Multitask networks
author (primary)	ARLID cav_un_auth*0242543 name1 Dedecius name2 Kamil full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS institution UTIA-B full_dept Department of Adaptive Systems country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0263972 name1 Sečkárová name2 Vladimíra full_dept (cz) Adaptivní systémy full_dept Department of Adaptive Systems department (cz) AS department AS institution UTIA-B full_dept Department of Adaptive Systems country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url http://library.utia.cas.cz/separaty/2017/AS/dedecius-0477044.pdf
cas_special	project ARLID cav_un_auth*0303543 project_id GP14-06678P agency GA ČR country CZ project ARLID cav_un_auth*0331019 project_id GA16-09848S agency GA ČR abstract (eng) Collaborative estimation of partially common parameters over ad hoc diffusion networks where the nodes directly communicate with their neighbors is a challenging task. The problem complexity is significantly high under the lack of knowledge which parameters are shared and among which network nodes. In this paper, we propose an adaptive framework suitable for this task. It is abstractly formulated in the Bayesian and information-theoretic paradigms and, therefore, versatile and easily applicable to a relatively wide class of models. If the observation models belong to the exponential family and the same functional types of prior probability distributions are used for estimation of the shared parameters, the method reduces to an analytically tractable variational algorithm extended by a procedure that passes messages among network nodes. A simulation example demonstrates that the collaboration improves estimation performance of both the shared and strictly local parameters, compared with the noncollaborative scenario. RIV BD FORD0 10000 FORD1 10100 FORD2 10102 reportyear 2018 num_of_auth 2 mrcbC52 4 A hod 4ah 20231122142603.3 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0274027 mrcbC64 1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, CYBERNETICS confidential S mrcbC86 3+4 Article Engineering Electrical Electronic mrcbC86 3+4 Article Engineering Electrical Electronic mrcbC86 3+4 Article Engineering Electrical Electronic mrcbT16-e ENGINEERINGELECTRICALELECTRONIC mrcbT16-j 1.559 mrcbT16-s 1.247 mrcbT16-B 87.621 mrcbT16-D Q1 mrcbT16-E Q1* arlyear 2017 mrcbTft \nSoubory v repozitáři: dedecius-0477044.pdf mrcbU14 85023175924 SCOPUS mrcbU24 PUBMED mrcbU34 000407100700002 WOS mrcbU63 cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 65 č. 19 2017 5153 5163