0427990 20240111140847.120140819235959.9 000343655306155 10.1109/ICASSP.2014.6854904 5 s. C cav_un_epca*0427988978-1-4799-2892-76736-6740PiscatawayIEEE2014 tensor decomposition PARAFAC CANDECOMP cav_un_auth*0274170 Phan A. H. JP cav_un_auth*0101212 Tichavský Petr Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0274171 Cichocki A. JP http://library.utia.cas.cz/separaty/2014/SI/tichavsky-0427990.pdf 347 kB GA14-13713S GA ČR CZ cav_un_auth*0303443 CANDECOMP/PARAFAC tensor decomposition (CPD) approximates multiway data by rank-1 tensors. Unlike matrix decomposition, the procedure which estimates the best rank-R tensor approximation through R sequential best rank-1 approximations does not work for tensors, because the deflation does not always reduce the tensor rank. In this paper we propose a novel deflation method for the problem in which rank R does not exceed the tensor dimensions. A rank-R CPD can be performed through (R−1) rank-1 reductions. At each deflation stage, the residue tensor is constrained to have a reduced multilinear rank. cav_un_auth*0305396 IEEE International Conference on Acoustics, Speech, and Signal Processing 2014 (ICASSP2014) Florence 04.05.2014-09.05.2014 IT 2015 BB 3 PO RVO:67985556 http://hdl.handle.net/11104/0235487 cav_un_auth*0303002 RIKEN JP S 2014 000343655306155 WOS 347 kB cav_un_epca*0427988 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 978-1-4799-2892-7 6736 6740 Piscataway IEEE 2014 IEEE Catalog Number: CFP14ICA-USB