0363810 20240111140759.620110913235959.9 4 s. CD ROM cav_un_epca*0363809978-1-4577-0568-7669-672NiceIEEE Signal Processing Society2011 nonnegative Tucker decomposition low-rank approximation face clustering 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-damped gauss-newton algorithm for nonnegative tucker decomposition.pdf 496 kB 1M0572 GA MŠk cav_un_auth*0001814 GA102/09/1278 GA ČR cav_un_auth*0253174 CEZ:AV0Z10750506 Algorithms based on alternating optimization for nonnegative Tucker decompositions (NTD) such as ALS, multiplicative least squares, HALS have been confirmed effective and efficient. However, those algorithms often converge very slowly. To this end, we propose a novel algorithm for NTD using the Levenberg-Marquardt technique with fast computation method to construct the approximate Hessian and gradient without building up the large-scale Jacobian. The proposed algorithm has been verified to overwhelmingly outperform “state-of-the-art” NTD algorithms for difficult benchmarks, and application of face clustering. cav_un_auth*0274147 2011 IEEE Statistical Signal Processing Workshop (SSP) Nice 28.06.2011-30.06.2011 FR 2012 BB 3 http://hdl.handle.net/11104/0199466 2011 496 kB cav_un_epca*0363809 2011 IEEE Statistical Signal Processing Workshop (SSP) Proceedings 978-1-4577-0568-7 669 672 Nice IEEE Signal Processing Society 2011