0396774 20240111140835.220131031235959.9 000324342900016 10.1109/TSP.2013.2269903 13 s. P cav_un_epca*02567271053-587X6119 (2013)4834-4846 Canonical polyadic decomposition tensor decomposition 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/2013/SI/tichavsky-0396774.pdf 4.2MB GA102/09/1278 GA ČR cav_un_auth*0253174 CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemometrics, telecommunication, data mining, neuroscience, separated representations. For an order- tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao products of all factor matrices except one. These products have the largest workload in most CP algorithms. In this paper, we propose a fast method to deal with this issue. Themethod also reduces the extra memory requirements of CP algorithms. As a result, we can accelerate the standard alternating CP algorithms 20–30 times for order-5 and order-6 tensors, and even higher ratios can be obtained for higher order tensors (e.g., N>=10). The proposed method is more efficient than the state-of-the-art ALS algorithm which operates two modes at a time (ALSo2) in the Eigenvector PLS toolbox, especially for tensors with order N>=5 and high rank. 2014 BB 3 RVO:67985556 http://hdl.handle.net/11104/0225512 ENGINEERINGELECTRICALELECTRONIC 3.592 0.439 7.II 0.07199 1.62 22913 508 2.074 Q1 94.605 90.927 Q1* Q1* 2013 000324342900016 WOS 4.2MB cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 61 č. 19 2013 4834 4846