0447197 20240111140907.220150925235959.9 84944706004 000363785500004 10.1007/978-3-319-22482-4_4 10 s. C cav_un_epca*0447195978-3-319-22482-40302-974331-40HeidelbergSpringer2015VincentEmmanuelYeredorArieKoldovskýZbyněkTichavskýPetr Canonical polyadic decomposition PARAFAC deflation 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/2015/SI/tichavsky-0447197.pdf 365 kB GA14-13713S GA ČR CZ cav_un_auth*0303443 CANDECOMP/PARAFAC (CP) approximates multiway data by a sum of rank-1 tensors. Our recent study has presented a method to rank-1 tensor deflation, i.e. sequential extraction of rank-1 tensor components. In this paper, we extend the method to block deflation problem. When at least two factor matrices have full column rank, one can extract two rank-1 tensors simultaneously, and rank of the data tensor is reduced by 2. For decomposition of order-3 tensors of size R×R×R and rank-R, the block deflation has a complexity of O(R^3) per iteration which is lower than the cost O(R^4) of the ALS algorithm for the overall CP decomposition. cav_un_auth*0319419 Latent Variable Analysis and Signal Separation 12th International Conference, LVA/ICA 2015 Liberec 25.08.2015-28.08.2015 CZ 2016 BB 3 PR RVO:67985556 http://hdl.handle.net/11104/0249578 S 0.329 Q2 Q2 2015 84944706004 SCOPUS 000363785500004 WOS 365 kB cav_un_epca*0447195 Latent Variable Analysis and Signal Separation 978-3-319-22482-4 0302-9743 31 40 Heidelberg Springer 2015 LNCS 9237 Lecture Notes in Computer Science Vincent Emmanuel 340 Yeredor Arie 340 Koldovský Zbyněk 340 Tichavský Petr 340