E. Acar, R. Bro, and A. K. Smilde, Data Fusion in Metabolomics Using Coupled Matrix and Tensor Factorizations, Proceedings of the IEEE, pp.1602-1620, 2015.
DOI : 10.1109/JPROC.2015.2438719

E. Acar, D. M. Dunlavy, T. G. Kolda, and M. Mørup, Scalable tensor factorizations for incomplete data, Chemometrics and Intelligent Laboratory Systems, vol.106, issue.1, pp.41-56, 2011.
DOI : 10.1016/j.chemolab.2010.08.004

URL : http://arxiv.org/abs/1005.2197

E. Acar, T. G. Kolda, and D. M. Dunlavy, All-at-once optimization for coupled matrix and tensor factorizations, 2011.

E. Acar, A. J. Lawaetz, M. A. Rasmussen, and R. Bro, Structure-revealing data fusion model with applications in metabolomics, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.6023-6026, 2013.
DOI : 10.1109/EMBC.2013.6610925

E. Acar and B. Yener, Unsupervised Multiway Data Analysis: A Literature Survey, IEEE Transactions on Knowledge and Data Engineering, vol.21, issue.1, pp.6-20, 2009.
DOI : 10.1109/TKDE.2008.112

R. Ballester-ripoll, S. K. Suter, and R. Pajarola, Analysis of tensor approximation for compression-domain volume visualization, Computers & Graphics, vol.47, pp.34-47, 2015.
DOI : 10.1016/j.cag.2014.10.002

H. Becker, Débruitage, séparation et localisation de sources EEG dans le contexte de l'´ epilepsie, Thèse de doctorat dirigée par Comon, P. et Albera, L. Automatique, 2014.

J. M. Bioucas-dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du et al., Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches . Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.5, issue.2, pp.354-379, 2012.
DOI : 10.1109/jstars.2012.2194696

URL : https://hal.archives-ouvertes.fr/hal-00760787

M. Boizard, R. Boyer, G. Favier, J. E. Cohen, and P. Comon, Performance estimation for tensor CP decomposition with structured factors, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3482-3486, 2015.
DOI : 10.1109/ICASSP.2015.7178618

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

C. C. Borel and S. A. , Nonlinear spectral mixing models for vegetative and soil surfaces. Remote sensing of environment, pp.403-416, 1994.
DOI : 10.1016/0034-4257(94)90107-4

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, pp.1-122, 2011.
DOI : 10.1561/2200000016

S. Boyd and L. Vandenberghe, Convex optimization, 2004.

J. P. Boyle and R. L. Dykstra, A Method for Finding Projections onto the Intersection of Convex Sets in Hilbert Spaces, Advances in order restricted statistical inference, pp.28-47, 1986.
DOI : 10.1007/978-1-4613-9940-7_3

R. Bro, Parafac. tutorial and applications. Chemometrics and intelligent laboratory systems, pp.149-171, 1997.

R. Bro, R. Bro, C. A. Andersson, and H. A. Kiers, Multi-way analysis in the food industry: models, algorithms, and applications The Netherlands Parafac2-part ii. modeling chromatographic data with retention time shifts, Journal of Chemometrics, vol.13, pp.3-4295, 1998.

R. Bro and S. Jong, A fast non-negativity-constrained least squares algorithm, Journal of Chemometrics, vol.11, issue.5, pp.393-401, 1997.
DOI : 10.1002/(SICI)1099-128X(199709/10)11:5<393::AID-CEM483>3.0.CO;2-L

R. Bro, R. A. Harshman, N. D. Sidiropoulos, and M. E. Lundy, Modeling multi-way data with linearly dependent loadings, Journal of Chemometrics, vol.16, issue.7-8, pp.324-340, 2009.
DOI : 10.1002/cem.1206

R. Bro and H. A. Kiers, A new efficient method for determining the number of components in PARAFAC models, Journal of Chemometrics, vol.398, issue.5, pp.274-286, 2003.
DOI : 10.1002/cem.801

R. Cabral-farias, J. E. Cohen, and P. Comon, Exploring Multimodal Data Fusion Through Joint Decompositions with Flexible Couplings, IEEE Transactions on Signal Processing, vol.64, issue.18, 2016.
DOI : 10.1109/TSP.2016.2576425

URL : https://hal.archives-ouvertes.fr/hal-01158082

R. Cabral-farias, J. E. Cohen, C. Jutten, and P. Comon, Joint Decompositions with Flexible Couplings, Latent Variable Analysis and Signal Separation, pp.119-126, 2015.
DOI : 10.1007/978-3-319-22482-4_14

URL : https://hal.archives-ouvertes.fr/hal-01135920

C. F. Caiafa and A. Cichocki, Multidimensional compressed sensing and their applications, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.34, issue.7, pp.355-380, 2013.
DOI : 10.1002/widm.1108

URL : http://onlinelibrary.wiley.com/doi/10.1002/widm.1108/pdf

F. Caland, S. Miron, D. Brie, and C. Mustin, A blind sparse approach for estimating constraint matrices in paralind data models, Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, pp.839-843, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00734259

J. D. Carroll and J. Chang, 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

J. D. Carroll, S. Pruzansky, and J. B. , Candelinc: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters, Psychometrika, vol.22, issue.1, pp.3-24
DOI : 10.1007/BF02293596

L. Chiantini and G. Ottaviani, On Generic Identifiability of 3-Tensors of Small Rank, SIAM Journal on Matrix Analysis and Applications, vol.33, issue.3, pp.1018-1037, 2012.
DOI : 10.1137/110829180

A. Cichocki, R. Zdunek, A. H. Phan, and S. Amari, Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation, 2009.
DOI : 10.1002/9780470747278

J. Cohen and P. Comon, On almost sure identifiability of non multilinear tensor decomposition, Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European, pp.2245-2249, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01059060

J. E. Cohen, About notations in multiway array processing. arXiv preprint, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01227873

J. E. Cohen, R. Farias, and P. Comon, Analyse de grandes donnees ten-sorielles couplees, XXVeme colloque GRETSI 2015

J. E. Cohen, R. Farias, and P. Comon, Fast Decomposition of Large Nonnegative Tensors, IEEE Signal Processing Letters, vol.22, issue.7, pp.22862-866, 2015.
DOI : 10.1109/LSP.2014.2374838

URL : https://hal.archives-ouvertes.fr/hal-01069069

J. E. Cohen, R. Farias, and P. Comon, Joint tensor compression for coupled canonical polyadic decompositions, 2016 24th European Signal Processing Conference (EUSIPCO), 2016.
DOI : 10.1109/EUSIPCO.2016.7760656

URL : https://hal.archives-ouvertes.fr/hal-01326132

J. E. Cohen, P. Comon, and X. Luciani, Correcting inner filter effects, a non multilinear tensor decomposition method, Chemometrics and Intelligent Laboratory Systems, vol.150, pp.29-40, 2016.
DOI : 10.1016/j.chemolab.2015.11.002

URL : https://hal.archives-ouvertes.fr/hal-01158152

J. E. Cohen, K. Usevich, and P. Comon, A Tour of Constrained Tensor Canonical Polyadic Decomposition. working paper or preprint, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01311795

P. L. Combettes and J. Pesquet, Primal-dual splitting algorithm for solving inclusions with mixtures of composite, lipschitzian, and parallel-sum type monotone operators. Set-Valued and variational analysis, pp.307-330, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00794044

P. Comon, Tensors : A brief introduction, IEEE Signal Processing Magazine, vol.31, issue.3, pp.44-53, 2014.
DOI : 10.1109/MSP.2014.2298533

URL : https://hal.archives-ouvertes.fr/hal-00923279

P. Comon and C. Jutten, Handbook of Blind Source Separation: Independent component analysis and applications. Academic press, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00460653

L. Condat, A generic proximal algorithm for convex optimization?application to total variation minimization, Signal Processing Letters IEEE, vol.21, issue.8, pp.985-989, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01120544

M. Congedo, EEG Source Analysis. Accreditation to supervise research, 2013.
DOI : 10.1016/b978-0-12-382235-2.00002-0

URL : https://hal.archives-ouvertes.fr/tel-00880483

M. Congedo, M. Goyat, N. Tarrin, G. Ionescu, L. Varnet et al., Brain Invaders " : a prototype of an open-source p300-based video game working with the openvibe platform, 5th International Brain-Computer Interface Conference, pp.280-283, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00641412

L. and D. Lathauwer, Decompositions of a Higher-Order Tensor in Block Terms???Part II: Definitions and Uniqueness, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.3, pp.1033-1066, 2008.
DOI : 10.1137/070690729

L. De-lathauwer, B. De, J. Moor, and . Vandewalle, A Multilinear Singular Value Decomposition, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.4, pp.1253-1278, 2000.
DOI : 10.1137/S0895479896305696

L. , D. Lathauwer, and D. Nion, Decompositions of a higher-order tensor in block terms-part iii: Alternating least squares algorithms, SIAM journal on Matrix Analysis and Applications, vol.30, issue.3, pp.1067-1083, 2008.

D. Lathauwer and J. Vandewalle, Dimensionality reduction in higher-order signal processing and rank-(R1,R2,???,RN) reduction in multilinear algebra, Linear Algebra and its Applications, vol.391, issue.2, pp.31-55, 2004.
DOI : 10.1016/j.laa.2004.01.016

. Lieven-de-lathauwer, Blind Separation of Exponential Polynomials and the Decomposition of a Tensor in Rank-$(L_r,L_r,1)$ Terms, SIAM Journal on Matrix Analysis and Applications, vol.32, issue.4, pp.1451-1474, 2011.
DOI : 10.1137/100805510

V. , D. Silva, and L. H. Lim, Tensor rank and the ill-posedness of the best low-rank approximation problem, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.3, pp.1084-1127, 2008.

C. Deledalle, L. Denis, G. Poggi, F. Tupin, and L. Verdoliva, Exploiting Patch Similarity for SAR Image Processing: The nonlocal paradigm, IEEE Signal Processing Magazine, vol.31, issue.4, pp.3169-78, 2014.
DOI : 10.1109/MSP.2014.2311305

URL : https://hal.archives-ouvertes.fr/ujm-00957334

I. Domanov and L. De-lathauwer, On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors---Part II: Uniqueness of the Overall Decomposition, SIAM Journal on Matrix Analysis and Applications, vol.34, issue.3, pp.876-903, 2013.
DOI : 10.1137/120877258

I. Domanov and L. De-lathauwer, Canonical Polyadic Decomposition of Third-Order Tensors: Reduction to Generalized Eigenvalue Decomposition, SIAM Journal on Matrix Analysis and Applications, vol.35, issue.2, pp.636-660, 2014.
DOI : 10.1137/130916084

URL : http://arxiv.org/abs/1312.2848

I. Domanov and L. De-lathauwer, Generic Uniqueness of a Structured Matrix Factorization and Applications in Blind Source??Separation, IEEE Journal of Selected Topics in Signal Processing, vol.10, issue.4, pp.701-711, 2016.
DOI : 10.1109/JSTSP.2016.2526971

L. Drumetz, S. Henrot, M. A. Veganzones, J. Chanussot, and C. Jutten, Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, p.2015, 2015.
DOI : 10.1109/TIP.2016.2579259

URL : https://hal.archives-ouvertes.fr/hal-01165114

J. W. Emsley, J. Feeney, and L. H. Sutcliffe, High resolution nuclear magnetic resonance spectroscopy Link prediction via generalized coupled tensor factorisation . arXiv preprint, 1965.

N. Gillis, Nonnegative matrix factorization: Complexity, algorithms and applications, 2011.

J. D. Gorman and A. O. Hero, Lower bounds for parametric estimation with constraints. Information Theory, IEEE Transactions on, vol.36, issue.6, pp.1285-1301, 1990.
DOI : 10.1109/18.59929

W. Hackbusch, Tensor spaces and numerical tensor calculus, 2012.
DOI : 10.1007/978-3-642-28027-6

B. Hapke, Theory of reflectance and emittance spectroscopy, 2012.

R. A. Harshman, Foundations of the parafac procedure: Models and conditions for an " explanatory " multi-modal factor analysis, 1970.

R. A. Harshman, Parafac2: Mathematical and technical notes. UCLA working papers in phonetics, p.122215, 1972.

R. A. Harshman, S. Hong, and M. E. Lundy, Shifted factor analysis?Part I: Models and properties, Journal of Chemometrics, vol.72, issue.7, pp.363-378, 2003.
DOI : 10.1002/cem.808

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

R. A. Harshman and M. E. Lundy, Data preprocessing and the extended PARAFAC model. Research Methods for Multimode Data Analysis, pp.216-284, 1984.

C. Hildreth, A quadratic programming procedure Naval research logistics quarterly, pp.79-85, 1957.

F. Hitchcock, The Expression of a Tensor or a Polyadic as a Sum of Products, Journal of Mathematics and Physics, vol.6, issue.1-4, pp.164-189, 1927.
DOI : 10.1002/sapm192761164

G. Hollander, P. Dreesen, M. Ishteva, and J. Schoukens, Weighted tensor decomposition for approximate decoupling of multivariate polynomials. arxiv e-prints, 2016.

S. Hong and R. A. Harshman, Shifted factor analysis?Part II: Algorithms, Journal of Chemometrics, vol.805, issue.7, pp.379-388, 2003.
DOI : 10.1002/cem.809

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

S. Hong and R. A. Harshman, Shifted factor analysis?Part III:N-way generalization and application, Journal of Chemometrics, vol.10, issue.7, pp.389-399, 2003.
DOI : 10.1002/cem.810

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

K. Huang, N. D. Sidiropoulos, and A. P. Liavas, A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization, IEEE Transactions on Signal Processing, vol.64, issue.19, 2015.
DOI : 10.1109/TSP.2016.2576427

URL : http://arxiv.org/abs/1506.04209

B. Jørgensen, The theory of exponential dispersion models and analysis of deviance. Number 51, 1992.

H. A. Kiers, Towards a standardized notation and terminology in multiway analysis, Journal of Chemometrics, vol.56, issue.3, pp.105-122, 2000.
DOI : 10.1002/1099-128X(200005/06)14:3<105::AID-CEM582>3.0.CO;2-I

H. A. Kiers, J. M. Berge, and R. Bro, Parafac2-part i. a direct fitting algorithm for the parafac2 model, Journal of Chemometrics, vol.13, pp.3-4275, 1999.

T. G. Kolda and B. W. Bader, Tensor decompositions and applications. SIAM rev, pp.455-500, 2009.
DOI : 10.1137/07070111x

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

L. Korczowski, M. Congedo, and C. Jutten, Single-trial classification of multi-user P300-based Brain-Computer Interface using riemannian geometry, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1769-1772, 2015.
DOI : 10.1109/EMBC.2015.7318721

URL : https://hal.archives-ouvertes.fr/hal-01191913

S. Kotz and N. L. Johnson, Breakthroughs in statistics: Foundations and basic theory, 2012.

P. Wim, . Krijnen, K. Theo, A. Dijkstra, and . Stegeman, On the non-existence of optimal solutions and the occurrence of " degeneracy " in the candecomp/parafac model, Psychometrika, vol.73, issue.3, pp.431-439, 2008.

D. J. Krusienski, E. W. Sellers, F. Cabestaing, S. Bayoudh, D. J. Mcfarland et al., A comparison of classification techniques for the P300 Speller, Journal of Neural Engineering, vol.3, issue.4, p.299, 2006.
DOI : 10.1088/1741-2560/3/4/007

URL : https://hal.archives-ouvertes.fr/hal-00521054

C. L. Lawson and R. J. Hanson, Solving least squares problems, SIAM, vol.15, 1995.
DOI : 10.1137/1.9781611971217

D. D. Lee and H. S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature, vol.401, issue.6755, pp.788-791, 1999.

A. P. Liavas and N. D. Sidiropoulos, Parallel Algorithms for Constrained Tensor Factorization via Alternating Direction Method of Multipliers, IEEE Transactions on Signal Processing, vol.63, issue.20, pp.5450-5463, 2015.
DOI : 10.1109/TSP.2015.2454476

L. H. Lim and P. Comon, Nonnegative approximations of nonnegative tensors, Journal of Chemometrics, vol.36, issue.3, pp.432-441, 2009.
DOI : 10.1002/cem.1244

URL : https://hal.archives-ouvertes.fr/hal-00410056

L. H. Lim and P. Comon, Multiarray signal processing: Tensor decomposition meets compressed sensing, Comptes Rendus M??canique, vol.338, issue.6, pp.311-320, 2010.
DOI : 10.1016/j.crme.2010.06.005

URL : https://hal.archives-ouvertes.fr/hal-00512271

L. H. Lim and P. Comon, Blind multilinear identification. Information Theory, IEEE Transactions on, vol.60, issue.2, pp.1260-1280, 2014.
DOI : 10.1109/tit.2013.2291876

URL : https://hal.archives-ouvertes.fr/hal-00763275

X. Liu and N. D. Sidiropoulos, Cramér-Rao lower bounds for low-rank decomposition of multidimensional arrays, IEEE Trans. Signal Process, vol.49, issue.9, pp.2074-2086, 2001.

X. Luciani, S. Mounier, R. Redon, and A. Bois, A simple correction method of inner filter effects affecting FEEM and its application to the PARAFAC decomposition, Chemometrics and Intelligent Laboratory Systems, vol.96, issue.2, pp.227-238, 2009.
DOI : 10.1016/j.chemolab.2009.02.008

URL : https://hal.archives-ouvertes.fr/hal-01479823

K. Madsen, H. B. Nielsen, and O. Tingleff, Methods for non-linear least squares problems, 2004.

J. Mairal, J. Ponce, G. Sapiro, A. Zisserman, and F. R. Bach, Supervised dictionary learning, Advances in neural information processing systems, pp.1033-1040, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00322431

S. G. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Mørup, L. K. Hansen, S. M. Arnfred, L. H. Lim, and K. H. Madsen, Shift-invariant multilinear decomposition of neuroimaging data, NeuroImage, vol.42, issue.4, pp.1439-1450, 2008.
DOI : 10.1016/j.neuroimage.2008.05.062

M. Mørup, L. K. Hansen, and K. H. Madsen, Modeling latency and shape changes in trial based neuroimaging data, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp.439-443, 2011.
DOI : 10.1109/ACSSC.2011.6190037

M. Mørup, L. K. Hansen, M. Sidse, and . Arnfred, Algorithms for Sparse Nonnegative Tucker Decompositions, Neural Computation, vol.5, issue.8, pp.2112-2131, 2008.
DOI : 10.1016/S0167-8655(01)00070-8

D. Muti and S. Bourennane, Survey on tensor signal algebraic filtering, Signal Processing, vol.87, issue.2, pp.237-249, 2007.
DOI : 10.1016/j.sigpro.2005.12.016

URL : https://hal.archives-ouvertes.fr/hal-00082704

R. Olfati-saber, A. Fax, and R. M. Murray, Consensus and Cooperation in Networked Multi-Agent Systems, Proceedings of the IEEE, vol.95, issue.1, pp.215-233, 2007.
DOI : 10.1109/JPROC.2006.887293

N. Pustelnik, J. Pesquet, and C. Chaux, Relaxing Tight Frame Condition in Parallel Proximal Methods for Signal Restoration, IEEE Transactions on Signal Processing, vol.60, issue.2, pp.968-973, 2012.
DOI : 10.1109/TSP.2011.2173684

URL : https://hal.archives-ouvertes.fr/hal-00692256

C. R. Rao, Information and accuracy attainable in the estimation of statistical parameters. Bull Calcutta, Math. Soc, vol.37, pp.81-91, 1945.

?. A. Rinnan and C. M. Andersson, Handling of first-order rayleigh scatter in parafac modelling of fluorescence excitation?emission data. Chemometrics and intelligent laboratory systems, pp.91-99, 2005.

B. Rivet and J. E. Cohen, Modeling time warping in tensor decomposition, 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016.
DOI : 10.1109/SAM.2016.7569733

S. Sahnoun, E. Djermoune, D. Brie, and P. Comon, A simultaneous sparse approximation method for multidimensional harmonic retrieval, Signal Processing, vol.131, 2015.
DOI : 10.1016/j.sigpro.2016.07.029

URL : https://hal.archives-ouvertes.fr/hal-01174780

L. Schwartz, Y. Bamberger, and J. Bourguignon, Les tenseurs, 1977.

N. Seichepine, S. Essid, C. Fevotte, and O. Cappé, Soft Nonnegative Matrix Co-Factorization, IEEE Transactions on Signal Processing, vol.62, issue.22, pp.5940-5949, 2014.
DOI : 10.1109/TSP.2014.2360141

URL : https://hal.archives-ouvertes.fr/hal-01116863

A. P. Singh and G. J. Gordon, Relational learning via collective matrix factorization, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.650-658, 2008.
DOI : 10.1145/1401890.1401969

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Sørensen and L. De-lathauwer, Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank-?L r,n , L r,n , 1q terms. Part I: Uniqueness, p.2013

M. Sørensen, I. Domanov, D. Nion, and L. De-lathauwer, Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank-?L r,n , L r,n , 1q terms. Part II: Algorithms, p.2013

M. Sørensen and L. De-lathauwer, Blind Signal Separation via Tensor Decomposition With Vandermonde Factor: Canonical Polyadic Decomposition, IEEE Transactions on Signal Processing, vol.61, issue.22, pp.615507-5519, 2013.
DOI : 10.1109/TSP.2013.2276416

M. Sørensen, L. De-lathauwer, P. Comon, S. Icart, and L. Deneire, Canonical Polyadic Decomposition with a Columnwise Orthonormal Factor Matrix, SIAM Journal on Matrix Analysis and Applications, vol.33, issue.4, pp.1190-1213, 2012.
DOI : 10.1137/110830034

A. Stegeman and A. L. De-almeida, Uniqueness Conditions for Constrained Three-Way Factor Decompositions with Linearly Dependent Loadings, SIAM Journal on Matrix Analysis and Applications, vol.31, issue.3, pp.311469-1490, 2009.
DOI : 10.1137/080743354

A. Stegeman and N. D. Sidiropoulos, On Kruskal???s uniqueness condition for the Candecomp/Parafac decomposition, Linear Algebra and its Applications, vol.420, issue.2-3, pp.540-552, 2007.
DOI : 10.1016/j.laa.2006.08.010

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

P. Tichavsky, A. H. Phan, and Z. Koldovsky, Cramér-Rao-induced bounds for candecomp/parafac tensor decomposition, Signal Processing IEEE Transactions on, issue.8, pp.611986-1997, 2013.
DOI : 10.1109/tsp.2013.2245660

M. E. Timmerman and H. A. Kiers, Three-way component analysis with smoothness constraints, Computational Statistics & Data Analysis, vol.40, issue.3, pp.447-470, 2002.
DOI : 10.1016/S0167-9473(02)00059-2

L. R. Tucker, Some mathematical notes on three-mode factor analysis, Psychometrika, vol.64, issue.3, pp.279-311, 1966.
DOI : 10.1007/BF02289464

H. L. Van-trees and K. L. Bell, Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking, 2007.
DOI : 10.1109/9780470544198

M. A. Veganzones, J. E. Cohen, R. Cabral-farias, J. Chanussot, and P. Comon, Nonnegative tensor cp decomposition of hyperspectral data. Geoscience and Remote Sensing, IEEE Transactions on, issue.99, pp.1-12, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01134470

M. A. Veganzones, J. E. Cohen, R. Cabral-farias, R. Marrero, J. Chanussot et al., Multilinear spectral unmixing of hyperspectral multiangle images, 2015 23rd European Signal Processing Conference (EUSIPCO), pp.744-748, 2015.
DOI : 10.1109/EUSIPCO.2015.7362482

URL : https://hal.archives-ouvertes.fr/hal-01158900

M. A. Veganzones, J. E. Cohen, R. Cabral-farias, K. Usevich, L. Drumetz et al., Canonical polyadic decomposition of hyperspectral patch tensors, 2016 24th European Signal Processing Conference (EUSIPCO), 2016.
DOI : 10.1109/EUSIPCO.2016.7760634

URL : https://hal.archives-ouvertes.fr/hal-01382362

M. A. Veganzones, S. Douté, J. E. Cohen, R. Cabral-farias, J. Chanussot et al., Nonnegative cp decomposition of multiangle hyperspectral data: a case study on CRISM observations of martian icy surface, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, p.2016, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01382360

W. Wang and M. A. Carreira-perpinán, Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application. arXiv preprint, 2013.

H. Whitney, Tensor products of Abelian groups, Duke Mathematical Journal, vol.4, issue.3, pp.495-528, 1938.
DOI : 10.1215/S0012-7094-38-00442-9

B. Yang, X. Fu, and N. D. Sidiropoulos, Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering, IEEE Transactions on Signal Processing, vol.65, issue.1, 2016.
DOI : 10.1109/TSP.2016.2614491

URL : http://arxiv.org/abs/1605.06711

K. Y. Y?lmaz, A. T. Cemgil, and U. Simsekli, Generalised coupled tensor factorisation, Adv. Neural. Inf. Process. Syst, pp.2151-2159, 2011.

Y. K. Yilmaz and A. T. , Alpha/beta divergences and tweedie models, 2012.

Y. Zhang, G. Zhou, Q. Zhao, A. Cichocki, and X. Wang, Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation, Neurocomputing, vol.198, pp.148-154, 2016.
DOI : 10.1016/j.neucom.2015.08.122