0363806 20240111140759.620110913235959.9 4 s. CD ROM cav_un_epca*0363805978-1-4577-0569-4581-584NiceIEEE Signal Processing Society2011 INDSCAL PARAFAC tensor decomposition cav_un_auth*0108100 Koldovský Zbyněk 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*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 http://library.utia.cas.cz/separaty/2011/SI/tichavsky-stability analysis and fast damped-gauss-newton algorithm for indscaltensor decomposition.pdf 430 kB 1M0572 GA MŠk cav_un_auth*0001814 GA102/09/1278 GA ČR cav_un_auth*0253174 GAP103/11/1947 GA ČR CZ cav_un_auth*0301478 CEZ:AV0Z10750506 INDSCAL is a special case of the CANDECOMP-PARAFAC (CP) decomposition of three or more-way tensors, where two factor matrices are equal. This paper provides a stability analysis of INDSCAL that is done by deriving the Cram'er-Rao lower bound (CRLB) on variance of an unbiased estimate of the tensor parameters from its noisy observation (the tensor plus a Gaussian random tensor). The existence of the bound reveals necessary conditions for the essential uniqueness of the INDSCAL decomposition. This is compared with previous results on CP. Next, analytical expressions for the inverse of the Hessian matrix, which is needed to compute the CRLB, are used in a damped Gaussian (Levenberg-Marquardt) algorithm, which gives a novel method for INDSCAL having a lower computational complexity. cav_un_auth*0274146 2011 IEEE Statistical Signal Processing Workshop (SSP) Nice 28.06.2011-30.06.2011 FR 2012 BB 3 http://hdl.handle.net/11104/0199463 2011 430 kB cav_un_epca*0363805 2011 IEEE Statistical Signal Processing Workshop (SSP) Proceedings 978-1-4577-0569-4 581 584 Nice IEEE Signal Processing Society 2011