0472586 20240111140936.320170313235959.9 85023755669 000414286202143 10.13140/RG.2.2.29610.82882 5 s. C cav_un_epca*0472585978-1-5090-4116-92542-2546New OrleansIEEE2017 tensor decomposition canonical polyadic decomposition PARAFAC alternating least squares 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/2017/SI/tichavsky-0472586.pdf 123 kB cav_un_auth*0345929 GA17-00902S GA ČR Canonical polyadic decomposition (CPD), also known as PARAFAC, is a representation of a given tensor as a sum of rank-one tensors. Traditional method for accomplishing CPD is the alternating least squares (ALS) algorithm. This algorithm is easy to implement with very low computational complexity per iteration. A disadvantage is that in difficult scenarios, where factor matrices in the decomposition contain nearly collinear columns, the number of iterations needed to achieve convergence might be very large. In this paper, we propose a modification of the algorithm which has similar complexity per iteration as ALS, but in difficult scenarios it needs a significantly lower number of iterations. cav_un_auth*0344245 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2017 20170305 20170309 New Orleans US BB 10000 10100 10103 2018 3 PO RVO:67985556 http://hdl.handle.net/11104/0271355 S n.a. Proceedings Paper Acoustics|Engineering Electrical Electronic 2017 85023755669 SCOPUS PUBMED 000414286202143 WOS 123 kB cav_un_epca*0472585 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2017 978-1-5090-4116-9 2542 2546 New Orleans IEEE 2017 CFP17ICA-USB