0542514 20240103225818.120210520235959.9 10.1109/ICASSP39728.2021.9414606 5 s. P cav_un_epca*0542513978-1-7281-7605-54690-4694PiscatawayIEEE2021 CANDECOMP PARAFAC rank minimization cav_un_auth*0382249 Phan A. H. RU 50 cav_un_auth*0101212 Tichavský Petr UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics 20 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0399439 Sobolev K. RU 10 cav_un_auth*0399440 Sozykin K. RU 10 cav_un_auth*0399441 Ermilov D. RU 5 cav_un_auth*0382250 Cichocki A. RU 5 http://library.utia.cas.cz/separaty/2021/SI/tichavsky-0542514.pdf This paper proposes a constrained canonical polyadic (CP) tensor decomposition method with low-rank factor matrices. In this way, we allow the CP decomposition with high rank while keeping the number of the model parameters small. First, we propose an algorithm to decompose the tensors into factor matrices of given ranks. Second, we propose an algorithm which can determine the ranks of the factor matrices automatically, such that the fitting error is bounded by a user- selected constant. The algorithms are verified on the decomposition of a tensor of the MNIST hand-written image dataset. cav_un_auth*0409360 IEEE International Conference on Acoustics, Speech, and Signal Processing 2021 20210606 20210611 Toronto CA BB 20000 20200 20201 2022 6 PR RVO:67985556 http://hdl.handle.net/11104/0320289 S 2021 SCOPUS PUBMED WOS cav_un_epca*0542513 ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 978-1-7281-7605-5 4690 4694 Piscataway IEEE 2021 ICASSP 2021