0392903 20240111140831.420130613235959.9 000329611506024 10.1109/ICASSP.2013.6638809 5 s. C cav_un_epca*0392897978-1-4799-0355-95964-5968VancouverIEEE2013 tensor factorization Gauss-Newton method 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*0274170 Phan A. H. JP cav_un_auth*0274171 Cichocki A. JP http://library.utia.cas.cz/separaty/2013/SI/tichavsky-a further improvement of a fast damped gauss-newton algorithm for candecomp-parafac tensor decomposition.pdf 110kB GA102/09/1278 GA ČR cav_un_auth*0253174 In this paper, a novel implementation of the damped Gauss-Newton algorithm (also known as Levenberg-Marquart) for the CANDECOMP-PARAFAC (CP) tensor decomposition is proposed. The method is based on a fast inversion of the approximate Hessian for the problem. It is shown that the inversion can be computed on O(NR^6) operations, where N and R is the tensor order and rank, respectively. It is less than in the best existing state-of-the art algorithm with O(N^3R^6) operations. The damped Gauss-Newton algorithm is suitable namely for difficult scenarios, where nearly-colinear factors appear in several modes simultaneously. Performance of the method is shown on decomposition of large tensors (100 × 100 × 100 and 100 × 100 × 100 × 100) of rank 5 to 90. cav_un_auth*0291703 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2013 Vancouver 27.05.2013-31.05.2013 CA 2014 BB 3 PO RVO:67985556 http://hdl.handle.net/11104/0221812 2013 000329611506024 WOS 110kB cav_un_epca*0392897 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2013 978-1-4799-0355-9 5964 5968 Vancouver IEEE 2013 CFP13ICA-USB