bibtype J - Journal Article
ARLID 0391438
utime 20240111140829.4
mtime 20130404235959.9
WOS 000317398500010
SCOPUS 84875651157
DOI 10.1109/TSP.2013.2245660
title (primary) (eng) Cramér-Rao-Induced Bounds for CANDECOMP/ PARAFAC Tensor Decomposition
specification
page_count 12 s.
media_type P
serial
ARLID cav_un_epca*0256727
ISSN 1053-587X
title IEEE Transactions on Signal Processing
volume_id 61
volume 8 (2013)
page_num 1986-1997
keyword Canonical polyadic decomposition
keyword multilinear models
keyword stability
author (primary)
ARLID cav_un_auth*0101212
name1 Tichavský
name2 Petr
full_dept (cz) Stochastická informatika
full_dept (eng) Department of Stochastic Informatics
department (cz) SI
department (eng) SI
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0274170
name1 Phan
name2 A. H.
country JP
author
ARLID cav_un_auth*0108100
name1 Koldovský
name2 Zbyněk
full_dept (cz) Stochastická informatika
full_dept Department of Stochastic Informatics
department (cz) SI
department SI
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2013/SI/tichavsky-0391438.pdf
source_size 3MB
cas_special
project
project_id GA102/09/1278
agency GA ČR
ARLID cav_un_auth*0253174
project
project_id GAP103/11/1947
agency GA ČR
country CZ
ARLID cav_un_auth*0301478
abstract (eng) This paper presents a Cramér-Rao lower bound (CRLB) on the variance of unbiased estimates of factor matrices in Canonical Polyadic (CP) or CANDECOMP/PARAFAC (CP) decompositions of a tensor from noisy observations, (i.e., the tensor plus a random Gaussian-distributed tensor). A novel expression is derived for a bound on the mean square angular error of factors along a selected dimension of a tensor of an arbitrary dimension. Insightful expressions are derived for tensors of rank 1 and rank 2 of arbitrary dimension and for tensors of arbitrary dimension and rank, where two factor matrices have orthogonal columns. The results can be used as a gauge of performance of different approximate CP decomposition algorithms, prediction of their accuracy, and for checking stability of a given decomposition of a tensor (condition whether the CRLB is finite or not).
reportyear 2014
RIV BB
num_of_auth 3
mrcbC52 4 A 4a 20231122135558.1
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0220515
mrcbT16-e ENGINEERINGELECTRICALELECTRONIC
mrcbT16-f 3.592
mrcbT16-g 0.439
mrcbT16-h 7.II
mrcbT16-i 0.07199
mrcbT16-j 1.62
mrcbT16-k 22913
mrcbT16-l 508
mrcbT16-s 2.074
mrcbT16-4 Q1
mrcbT16-B 94.605
mrcbT16-C 90.927
mrcbT16-D Q1*
mrcbT16-E Q1*
arlyear 2013
mrcbTft \nSoubory v repozitáři: tichavsky-0391438.pdf
mrcbU14 84875651157 SCOPUS
mrcbU34 000317398500010 WOS
mrcbU56 3MB
mrcbU63 cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 61 č. 8 2013 1986 1997