A comparison of algorithms for fitting the PARAFAC model, Computational Statistics & Data Analysis, vol.50, issue.7, pp.1700-1734, 2006. ,
DOI : 10.1016/j.csda.2004.11.013
Tensor decompositions, alternating least squares and other tales, Journal of Chemometrics, vol.78, issue.8, pp.393-405, 2009. ,
DOI : 10.1016/j.laa.2009.01.014/
URL : https://hal.archives-ouvertes.fr/hal-00410057
Adaptive Algorithms to Track the PARAFAC Decomposition of a Third-Order Tensor, IEEE Transactions on Signal Processing, vol.57, issue.6, pp.2299-2310, 2009. ,
DOI : 10.1109/TSP.2009.2016885
Consensus-based in-network computation of the PARAFAC decomposition, 2013. ,
PARAFAC algorithms for large-scale problems, Neurocomputing, vol.74, issue.11, pp.1970-1984, 2011. ,
DOI : 10.1016/j.neucom.2010.06.030
Distributed average consensus with least-mean-square deviation, Journal of Parallel and Distributed Computing, vol.67, issue.1, pp.33-46, 2007. ,
DOI : 10.1016/j.jpdc.2006.08.010
Foundation of the PARAFAC procedure: models and conditions for an "explanatory" multimodal factor analysis, UCLA working papers in phonetics, vol.16, pp.1-84, 1970. ,
Analysis of individual differences in multidimensional scaling via an n-way generalization of ???Eckart-Young??? decomposition, Psychometrika, vol.12, issue.3, pp.283-319, 1970. ,
DOI : 10.1007/BF02310791
Fast linear iterations for distributed averaging, Systems & Control Letters, vol.53, issue.1, pp.65-78, 2004. ,
DOI : 10.1016/j.sysconle.2004.02.022
Finite-time average consensus based protocol for distributed estimation over AWGN channels, IEEE Conference on Decision and Control and European Control Conference, 2011. ,
DOI : 10.1109/CDC.2011.6160868
Distance-regular graphs, 1989. ,