[1] F. Hitchcock, “Multiple invariants and generalized rank of a p-way matrix or tensor”, *J. Mathematics and Physics *7, 39-79 (1927).

[2] L. Tucker, “Some mathematical notes on three-mode factor analysis”, *Psychometrika *31 (3), 279-311 (1966). [Crossref] [PubMed]

[3] L. De Lathauwer, B. De Moor, and J. Vandewalle, “A multilinear singular value decomposition”, *SIAM J. on Matrix Analysisand Applications *21, 1253-1278 (2000).

[4] T.G. Kolda and B.W. Bader, “Tensor decompositions and applications”, *SIAM Review *51 (3), 455-500 (2009). [Crossref]

[5] A. Cichocki, R. Zdunek, A.-H. Phan, and S. Amari, *Nonnegative Matrix and Tensor Factorizations: Applications to ExploratoryMulti-way Data Analysis and Blind Source Separation*, Wiley, Chichester, 2009.

[6] A. Cichocki, “Tensors decompositions: new concepts for brain data analysis?”, *J. Control, Measurement, and System Integration(SICE) *7, 507-517 (2011).

[7] L. De Lathauwer, B. De Moor, and J. Vandewalle, “On the best rank-1 and rank-(R1,R2, ...,Rn) approximation of higherorder tensors”, *SIAM J. on Matrix Analysis and Applications* 21 (4), 1324-1342 (2000).

[8] F. Miwakeichi, E. Martnez-Montes, P. Valds-Sosa, N. Nishiyama, H. Mizuhara, and Y. Yamaguchi, “Decomposing EEG data into space-time-frequency components using parallel factor analysis”, *NeuroImage *22 (3), 1035-1045 (2004). [Crossref] [PubMed]

[9] Z. He, A. Cichocki, S. Xie, and K. Choi, “Detecting the number of clusters in n-way probabilistic clustering”, *IEEE Trans.on Pattern Analysis and Machine Intelligence *32 (11), 2006- 2021 (2010).

[10] H.A. Phan and A. Cichocki, “Tensor decompositions for feature extraction and classification of high dimensional datasets”, *Nonlinear Theory and Its Applications, IEICE *1 (1), 37-68 (2010).

[11] R. Bro, “Multi-way analysis in the food industry: Models, algorithms & applications”, *Food Technology *309, http://curis.ku.dk/ws/files/13035961/Rasmus−Bro.pdf, (1998).

[12] A.H. Andersen and W.S. Rayens, “Structure-seeking multilinear methods for the analysis of fMRI data”, *NeuroImage *22 (2), 728-739 (2004). [Crossref] [PubMed]

[13] E. Mart´inez-Montes, P.A. Vald´es-Sosa, F. Miwakeichi, R. Goldman, and M. Cohen, “Concurrent EEG/fMRI analysis by multiway partial least squares”, *NeuroImage *22 (3), 1023- 1034 (2004).

[14] E. Acar, C. Aykut-Bingol, H. Bingol, R. Bro, and B. Yener, “Multiway analysis of epilepsy tensors”, *Bioinformatics* 23 (13), i10-i18 (2007). [PubMed]

[15] M. De Vos, A. Vergult, and L. De Lathauwer, “Canonical decomposition of ictal scalp EEG reliably detects the seizure onset zone”, *NeuroImage *37 (3), 844-854 (2007). [Crossref] [PubMed]

[16] M. Mørup, L.K. Hansen, and S.M. Arnfred, “Algorithms for sparse nonnegative Tucker decompositions”, *Neural Computation* 20 (8), 2112-2131 (2008). [Crossref] [PubMed]

[17] L. De Lathauwer, “A survey of tensor methods”, *IEEE Int.Symposiumon Circuits and Systems (ISCAS) *1, 2773-2776 (2009).

[18] V. De Silva and L.-H. Lim, “Tensor rank and the ill-posedness of the best low-rank approximation problem”, *SIAM J. on MatrixAnalysis and Applications *30 (3), 1084-1127 (2008).

[19] S. Weiland and F. Van Belzen, “Singular value decompositions and low rank approximations of tensors”, *IEEE Trans.on Signal Processing *58 (3), 1171-1182 (2010). [Crossref]

[20] L. De Lathauwer, “A link between the canonical decomposition in multilinear algebra and simultaneous matrix diagonalization”, *SIAM J. on Matrix Analysis and Applications *28 (3), 642-666 (2006).

[21] C.F. Beckmann and S.M. Smith, “Tensorial extensions of independent component analysis for multisubject fMRI analysis”, *NeuroImage *25 (1), 294-311 (2005). [PubMed] [Crossref]

[22] M. De Vos, L. De Lathauwer, and S. Van Huffel, “Algorithm for imposing SOBI-type constraints on the CP model”, *IEEEInt. Symp. Circuits and Systems (ISCAS).*, 2008, 1344-1347.

[23] M. Vasilescu and D. Terzopoulos, “Multilinear independent components analysis”, *IEEE Computer Society Conf. on ComputerVision and Pattern Recognition *1, 547-553 (2005).

[24] S. Unkel, A. Hannachi, N. Trendafilov, and I. Jolliffe, “Independent component analysis for three-way data with an application from atmospheric science”, *J. Agricultural, Biological,and Environmental Statistics *16, 319-338 (2011).

[25] A. Bell and T. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution”, *NeuralComputation *7, 1129-1159 (1995).

[26] A. Hyvarinen, J. Karhunen, and E. Oja, *Independent ComponentAnalysis*, Wiley, New York, 2001.

[27] A. Cichocki and S. Amari, *Adaptive Blind Signal and ImageProcessing: Learning Algorithms and Applications*, John Wiley & Sons, London, 2002.

[28] A. Karfoul, L. Albera, and L. De Lathauwer, “Iterative methods for the canonical decomposition of multi-way arrays: Application to blind underdetermined mixture identification”, *SignalProcessing *91 (8), 1789-1802 (2011).

[29] M. Zibulevsky and B. A. Pearlmutter, “Blind source separation by sparse decomposition in a signal dictionary”, *NeuralComputation *13 (4), 863-882 (2001).

[30] P. Georgiev, F. Theis, and A. Cichocki, “Sparse component analysis and blind source separation of underdetermined mixtures”, *IEEE Trans. on Neural Networks *16 (4), 992-996 (2005). [Crossref]

[31] D.D. Lee and H.S. Seung, “Learning the parts of objects by non-negative matrix factorization”, *Nature *401 (6755), 788- 791 (1999). [PubMed]

[32] B.W. Bader and T.G. Kolda, “MATLAB tensor toolbox version 2.5”, http://csmr.ca.sandia.gov/tgkolda/TensorToolbox/, (2012).

[33] G. Zhou and A. Cichocki, “Canonical polyadic decomposition based on a single mode blind source separation”, *IEEE SignalProcessing Letters *19 (8), 523-526 (2012).

[34] X. Feng and Z. Zhang, “The rank of a random matrix”, *AppliedMathematics and Computation *185 (1), 689-694 (2007).

[35] F.M. Fisher, *The Identification Problem in Econometrics*, McGraw-Hill, New York, 1966.

[36] M. De Vos, D. Nion, S. Van Huffel, and L. De Lathauwer, “A combination of parallel factor and independent component analysis”, *Tech. Rep.*, ftp://ftp.esat.kuleuven.ac.be/pub/sista/mdevos/reports/Ica−cp08.pdf (2008).

[37] D. Langers, “Unbiased group-level statistical assessment of independent component maps by means of automated retrospective matching”, *Human Brain Mapping *31, 727-742 (2010). [PubMed]

[38] A. Cichocki, “Generalized component analysis and blind source separation methods for analyzing mulitchannel brain signals”, *Statistical and Process Models for Cognitive Neuroscienceand Aging *1, 201-272 (2007).

[39] Z. Lin, M. Chen, and Y. Ma, “The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices”, *ArXiv e-prints*, 1009.5055 (2010).

[40] P. Drineas, R. Kannan, and M.W. Mahoney, “Fast Monte Carlo algorithms for matrices II: computing a low-rank approximation to a matrix”, *SIAM J. on Computing *36 (1), 158-183 (2006).

[41] M.W. Mahoney and P. Drineas, “CUR matrix decompositions for improved data analysis”, *Proc. Nat. Ac. Sciences *106 (3), 697-702 (2009).

[42] C.F. Caiafa and A. Cichocki, “Generalizing the column-row matrix decomposition to multi-way arrays”, *Linear Algebra andIts Applications *433 (3), 557-573 (2010).

[43] E. Ceulemans and H.A.L. Kiers, “Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method”, *British J. Mathematicaland Statistical Psychology *59 (1), 133-150 (2006).

[44] H.A.L. Kiers and A. der Kinderen, “A fast method for choosing the numbers of components in tucker3 analysis”, *BritishJ. Mathematical and Statistical Psychology *56 (1), 119-125 (2003).

[45] M.O. Ulfarsson and V. Solo, “Dimension estimation in noisy pca with sure and random matrix theory”, *IEEE Trans. onSignal Processing *56 (12), 5804-5816 (2008). [Crossref]

[46] A. Cichocki, S. Amari, and K. Siwek, “ICALAB toolbox”, http://www.bsp.brain.riken.jp/ICALAB (2007).

[47] Z. Koldovsky, P. Tichavsky, and E. Oja, “Efficient variant ofalgorithm FastICA for independent component analysis attaining the Cramer-Rao lower bound”, *IEEE Trans. on Neural Networks* 17 (5), 1265-1277 (2006). [Crossref]

[48] A. Belouchrani, K. AbedMeraim, and J.F. Cardoso, “A blind source separation technique using second-order statistics”, *IEEE Trans. on Signal Processing *45 (2), 434-444 (1997). [Crossref]

[49] C.A. Andersson and R. Bro, “The N-way toolbox for MATLAB”, http://www.models.life.ku.dk/source/nwaytoolbox/ (2000).

[50] A.H. Phan and A. Cichocki, “Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification”, *Neurocomputing *74 (11), 1956-1969 (2011). [Crossref]

[51] R. Zdunek, H.A. Phan, and A. Cichocki, “Damped Newton iterations for nonnegative matrix factorization”, *Australian J. Intelligent**Information Processing Systems *12 (1), 16-22 (2010).

[52] G. Zhou, A. Cichocki, and S. Xie, “Fast nonnegative matrix/ tensor factorization based on low-rank approximation”, *IEEE Trans. on Signal Processing *60 (6), 2928-2940 (2012). [Crossref]

[53] S.A. Nene, S.K. Nayar, and H. Murase, “Columbia object image library (COIL-20)”, http://www.cs.columbia. edu/ CAVE/software/softlib/coil-20.php (1996).

[54] L. Van Der Maaten and C. Detection, “Visualizing data using t-SNE”, *J. Machine Learning Research *9, 2579-2605 (2008).

[55] Y. Yang, D. Xu, and F. Nie, “Image clustering using local discriminant models and global integration”, *IEEE Trans. on**Image Processing *19 (10), 2761-2773 (2010). [Crossref]

[56] B.Z.Allison, D.J. McFarland, G. Schalk, S.D. Zheng, M. Jackson, and J.R. Wolpaw, “Towards an independent braincomputer interface using steady state visual evoked potentials”, *Clinical Neurophysiology *119 (2), 399-408 (2008). [Crossref]

[57] Y. Zhang, G. Zhou, Q. Zhao, A. Onishi, J. Jin, X. Wang, and A. Cichocki, “Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIs”, *Neural Information Processing*, Springer, Berlin, 2011.

[58] X. Gao, D. Xu, M. Cheng, and S. Gao, “A BCI-based environmental controller for the motion-disabled”, *IEEE Trans. on**Neural Systems and Rehabilitation Engineering *11 (2), 137- 140 (2003).

[59] Z. Wu and D. Yao, “Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs”, *J. Neural Engineering *5 (1), 36 (2008).

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