J. R. Lacowicz, Principle of fluorescence spectroscopy, 1999.
DOI : 10.1007/978-0-387-46312-4

P. Carroll and J. 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

R. A. Harshman, Foundation of the Parafac procedure: models and conditions for an explanatory multimodal factor analysis. UCLA Working papers in phonetics, pp.1-84, 1970.

R. Bro, PARAFAC. Tutorial and applications, Chemometrics and Intelligent Laboratory Systems, vol.38, issue.2, pp.149-171, 1997.
DOI : 10.1016/S0169-7439(97)00032-4

R. Bro, Multi-way analysis in the food industry: models, algorithms and applications, 1998.

X. Meng, A. J. Morris, and E. B. Martin, On-line monitoring of batch processes using a PARAFAC representation, Journal of Chemometrics, vol.25, issue.1, pp.65-81, 2003.
DOI : 10.1002/cem.776

C. A. Stedmon, S. Markager, and R. Bro, Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy, Marine Chemistry, vol.82, issue.3-4, pp.239-254, 2003.
DOI : 10.1016/S0304-4203(03)00072-0

C. A. Stedmon and S. Markager, Behaviour of the optical properties of coloured dissolved organic matter under conservative mixing, Estuarine, Coastal and Shelf Science, vol.57, issue.5-6
DOI : 10.1016/S0272-7714(03)00003-9

C. A. Stedmon and S. Markager, Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis, Limnology and Oceanography, vol.50, issue.2, pp.686-697, 2005.
DOI : 10.4319/lo.2005.50.2.0686

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

J. Costa, M. Haardt, and F. Roemer, Robust methods based on the hosvd for estimating the model order in PARAFAC models, 2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, pp.510-514, 2008.
DOI : 10.1109/SAM.2008.4606923

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

N. Sidiropoulos and R. Bro, On the uniqueness of multilinear decomposition of N-way arrays, Journal of Chemometrics, vol.14, issue.3, pp.229-239, 2000.
DOI : 10.1002/1099-128X(200005/06)14:3<229::AID-CEM587>3.3.CO;2-E

P. Paatero, Construction and analysis of degenerate PARAFAC models, Journal of Chemometrics, vol.8, issue.3, pp.285-299, 2000.
DOI : 10.1002/1099-128X(200005/06)14:3<285::AID-CEM584>3.0.CO;2-1

N. Vervliet and L. De-lathauwer, A Randomized Block Sampling Approach to Canonical Polyadic Decomposition of Large-Scale Tensors, IEEE Journal of Selected Topics in Signal Processing, vol.10, issue.2, pp.284-295, 2016.
DOI : 10.1109/JSTSP.2015.2503260

N. Sidiropoulos, E. E. Papalexakis, and C. Faloutsos, Parallel randomly compressed cubes, IEEE Signal Process. Mag. Special Issue on Big Data, issue.5, pp.3157-70, 2014.

P. G. Coble, Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy, Marine Chemistry, vol.51, issue.4, pp.325-346, 1996.
DOI : 10.1016/0304-4203(95)00062-3

A. Smilde, R. Bro, and P. Geladi, Multi-Way Analysis with applications in the chemical sciences, 2004.
DOI : 10.1002/0470012110

J. Royer, N. Thirion-moreau, P. Comon, R. Redon, and S. Mounier, A regularized nonnegative canonical polyadic decomposition algorithm with preprocessing for 3D fluorescence spectroscopy, Journal of Chemometrics, vol.35, issue.77, pp.253-265, 2015.
DOI : 10.1002/cem.2709
URL : https://hal.archives-ouvertes.fr/hal-01126693

A. Karami, M. Yazdi, and A. Asli, Noise Reduction of Hyperspectral Images Using Kernel Non-Negative Tucker Decomposition, Selected Topics in Signal Processing, pp.487-493, 2011.
DOI : 10.1109/JSTSP.2011.2132692

Q. Zhang, H. Wang, R. Plemmons, and P. Pauca, Tensors methods for hyperspectral data processing: a space object identification study, Journal of Optical Society of America A, 2008.

H. Wang and N. Ahuja, Compact representation of multidimensional data using tensor rank-one decomposition, Int'l Conf. Pattern Recognition, pp.44-47, 2004.

H. Wang and N. Ahuja, A Tensor Approximation Approach to??Dimensionality Reduction, International Journal of Computer Vision, vol.61, issue.1, pp.217-229, 2008.
DOI : 10.1007/s11263-007-0053-0

I. Kopriva and A. Cichocki, Blind decomposition of low-dimensional multi-spectral image by sparse component analysis, Journal of Chemometrics, vol.15, issue.11, pp.590-597, 2009.
DOI : 10.1002/cem.1257

I. Kopriva and A. Cichocki, Blind multispectral image decomposition by 3D nonnegative tensor factorization, Optics Letters, vol.34, issue.14, pp.2010-2012, 2009.
DOI : 10.1364/OL.34.002210

A. H. Andersen and W. S. Rayens, Structure-seeking multilinear methods for the analysis of fMRI data, NeuroImage, vol.22, issue.2, pp.728-739, 2004.
DOI : 10.1016/j.neuroimage.2004.02.026

A. S. Field and D. Graupe, Topographic component (Parallel Factor) analysis of multichannel evoked potentials: Practical issues in trilinear spatiotemporal decomposition, Brain Topography, vol.59, issue.4, pp.407-423, 1991.
DOI : 10.1007/BF01129000

A. Cichocki, R. Zdunek, A. H. Phan, and S. I. Amari, Non negative matrix and tensor factorizations: Application to exploratory multi-way data analysis and blind separation, 2009.

E. Chouzenoux, J. Pesquet, and A. Repetti, A block coordinate variable metric forward???backward algorithm, Journal of Global Optimization, vol.6, issue.3, pp.1-29, 2016.
DOI : 10.1007/s10898-016-0405-9
URL : https://hal.archives-ouvertes.fr/hal-00945918

J. Bolte, S. Sabach, and M. Teboulle, Proximal alternating linearized minimization for nonconvex and nonsmooth problems, Mathematical Programming, pp.459-494, 2014.
DOI : 10.1007/s10107-013-0701-9
URL : https://hal.archives-ouvertes.fr/hal-00916090

A. Repetti, E. Chouzenoux, and J. Pesquet, A preconditioned Forward-Backward approach with application to large-scale nonconvex spectral unmixing problems, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.
DOI : 10.1109/ICASSP.2014.6853847
URL : https://hal.archives-ouvertes.fr/hal-01077329

Y. Xu and W. Yin, A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion, SIAM Journal on Imaging Sciences, vol.6, issue.3, pp.1758-1789, 2013.
DOI : 10.1137/120887795

Y. Zhang, G. Zhou, 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

D. R. Hunter and K. Lange, A Tutorial on MM Algorithms, The American Statistician, vol.58, issue.1, pp.30-37, 2004.
DOI : 10.1198/0003130042836

C. Chaux, P. L. Combettes, J. Pesquet, and V. R. Wajs, A variational formulation for frame-based inverse problems, Inverse Problems, vol.23, issue.4, pp.1495-1518, 2007.
DOI : 10.1088/0266-5611/23/4/008
URL : https://hal.archives-ouvertes.fr/hal-00621883

A. Repetti, M. Q. Pham, L. Duval, E. Chouzenoux, and J. Pesquet, Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed <formula formulatype="inline"><tex Notation="TeX">${\ell _1}/{\ell _2}$</tex></formula> Regularization, IEEE Signal Processing Letters, vol.22, issue.5, pp.539-543, 2015.
DOI : 10.1109/LSP.2014.2362861

M. W. Mahoney, Randomized Algorithms for Matrices and Data, Machine Learning, pp.123-224, 2011.
DOI : 10.1201/b11822-37

V. Cevher, S. Becker, and M. Schmidt, Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics, IEEE Signal Processing Magazine, vol.31, issue.5, pp.3132-3175, 2014.
DOI : 10.1109/MSP.2014.2329397

E. Chouzenoux, J. Pesquet, and A. Repetti, Variable Metric Forward???Backward Algorithm for Minimizing the Sum of a Differentiable Function and a Convex Function, Journal of Optimization Theory and Applications, vol.21, issue.2, pp.107-132, 2014.
DOI : 10.1007/s10957-013-0465-7
URL : https://hal.archives-ouvertes.fr/hal-00789970

J. Moreau, Proximit?? et dualit?? dans un espace hilbertien, Bulletin de la Société mathématique de France, vol.79, pp.273-299, 1965.
DOI : 10.24033/bsmf.1625
URL : http://archive.numdam.org/article/BSMF_1965__93__273_0.pdf

P. L. Combettes and V. R. Wajs, Signal Recovery by Proximal Forward-Backward Splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.
DOI : 10.1137/050626090
URL : https://hal.archives-ouvertes.fr/hal-00017649

P. L. Combettes and J. Pesquet, A proximal decomposition method for solving convex variational inverse problems, Inverse Problems, vol.24, issue.6, p.27, 2008.
DOI : 10.1088/0266-5611/24/6/065014
URL : https://hal.archives-ouvertes.fr/hal-00692901

J. Hiriart-urruty and C. Lemarchal, Convex Analysis and Minimization Algorithms, 1993.
DOI : 10.1007/978-3-662-02796-7

C. G. Khatri and C. R. Rao, Solutions to some functional equations and their applications to characterization of probability distributions

D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, Advances in Neural Information Processing Systems 13, pp.556-562, 2001.

P. L. Combettes and J. Pesquet, Proximal Splitting Methods in Signal Processing, Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2010.
DOI : 10.1007/978-1-4419-9569-8_10
URL : https://hal.archives-ouvertes.fr/hal-00643807

X. T. Vu, C. Chaux, S. Maire, and N. Thirion-moreau, Study of different strategies for the Canonical Polyadic decomposition of nonnegative third order tensors with application to the separation of spectra in 3D fluorescence spectroscopy, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2014.
DOI : 10.1109/MLSP.2014.6958896
URL : https://hal.archives-ouvertes.fr/hal-01278483

R. Bro, PARAFAC. Tutorial and applications, Chemometrics and Intelligent Laboratory Systems, vol.38, issue.2, pp.149-171, 1997.
DOI : 10.1016/S0169-7439(97)00032-4

A. H. Phan and P. Tichavsk, Tichavsk`y, and A. Cichocki. Fast alternating ls algorithms for high order candecomp/parafac tensor factorizations

G. H. Paul, Introduction to Mathematical Statistics, 1984.

E. M. Carstea, S. Mounier, R. Redon, X. Luciani, C. Gadio et al., On-line parafac analysis of fluorescence spectra, International Workshop on Organic Matter Spectroscopy (WOMS'13), 2013.

R. G. Zepp, W. M. Sheldon, and M. A. Moran, Dissolved organic fluorophores in southeastern US coastal waters: correction method for eliminating Rayleigh and Raman scattering peaks in excitation???emission matrices, Marine Chemistry, vol.89, issue.1-4, pp.1-415, 2004.
DOI : 10.1016/j.marchem.2004.02.006

Z. Fan, O. Schroder, and J. Krahl, Analysis of diesel fuels/biodiesel blends and identification of biodiesel using time-resolved laser-induced fluorescence spectroscopy (trlfs), Appl Agric Forestry Res, vol.65, pp.1-14, 2015.

H. Attouch and J. Bolte, On the convergence of the proximal algorithm for nonsmooth functions involving analytic features, Mathematical Programming, vol.4, issue.1-2, pp.5-16, 2009.
DOI : 10.1007/s10107-007-0133-5
URL : https://hal.archives-ouvertes.fr/hal-00803898

S. ?ojasiewicz, Une propriété topologique des sous-ensembles analytiques réels, Editions du Centre National de le Recherche Scientifique, pp.87-89, 1963.

K. Kurdyka and A. Parusinski, w f -stratification of subanalytic functions and the ?ojasiewicz inequality, Comptes rendus de l'acadmie des Sciences, pp.129-133, 1994.