0341572 20240111140738.720100325235959.9 4 s. www cav_un_epca*0341650978-1-4244-4296-64114-4117DallasIEEE2010 tensor factorization multilinear models 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*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. pdf http://library.utia.cas.cz/separaty/2010/SI/tichavsky-simultanous search for all modes in multilinear models.pdf 187 kB 1M0572 GA MŠk cav_un_auth*0001814 GP102/07/P384 GA ČR CZ cav_un_auth*0227962 GA102/09/1278 GA ČR cav_un_auth*0253174 CEZ:AV0Z10750506 Parallel factor (PARAFAC) analysis is an extension of a low rank decomposition to higher way arrays, usually called tensors. Most of existing methods are based on an alternating least square (ALS) algorithm that proceeds iteratively, and minimizes a criterion (that is usually quadratic) of the fit with respect to individual factors one by one. Convergence of this approach is known to be slow, if some of the factor contain nearly co-linear vectors. This problem can be partly alleviated by an enhanced line search (ELS) by Rajih et al. (2008). In this paper we show that the method originally proposed by Paatero (1997), consisting in optimization with respect to all modes simultaneously, can be simplified, and can far outperform the ALS-ELS in ill--conditioned data in all modes. cav_un_auth*0261294 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing Dallas, TX 14.03.2010-19.03.2010 US 2011 BB http://hdl.handle.net/11104/0184511 2010 pdf 187 kB cav_un_epca*0341650 Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing 2010 978-1-4244-4296-6 4114 4117 Dallas IEEE 2010