0360026 20240111140756.320111108235959.9 4 s. cav_un_epca*0363842978-1-4577-0539-71988-1991PiscatawayIEEE2011 Multilinear models canonical polyadic decomposition nonegative tensor factorization 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/2011/SI/tichavsky-fast damped gauss-newton algorithm for nonnegative matrix factorization.pdf 217 kB 1M0572 GA MŠk cav_un_auth*0001814 GA102/09/1278 GA ČR cav_un_auth*0253174 CEZ:AV0Z10750506 Alternating optimization algorithms for canonical polyadic decomposition (with/without nonnegative constraints) often accompany update rules with low computational cost, but could face problems of swamps, bottlenecks, and slow convergence. All-at-once algorithms can deal with such problems, but always demand significant temporary extra-storage, and high computational cost. In this paper, we propose an allat- once algorithmwith lowcomplexity for sparse and nonnegative tensor factorization based on the damped Gauss-Newton iteration. Especially, for low-rank approximations, the proposed algorithm avoids building up Hessians and gradients, reduces the computational cost dramatically. Moreover, we proposed selection strategies for regularization parameters. The proposed algorithm has been verified to overwhelmingly outperform “state-of-the-art” NTF algorithms for difficult benchmarks, and for real-world application such as clustering of the ORL face database. cav_un_auth*0272319 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2011 Praha 22.05.2011-27.05.2011 CZ 2012 BB 3 http://hdl.handle.net/11104/0197677 2011 217 kB cav_un_epca*0363842 Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011 978-1-4577-0539-7 1988 1991 Piscataway IEEE 2011