S. Amari, T. Chen, and A. Cichocki, Stability Analysis of Learning Algorithms for Blind Source Separation, Neural Networks, vol.10, issue.8, pp.1345-1351, 1997.
DOI : 10.1016/S0893-6080(97)00039-7

[. Amari, A. Cichocki, and H. H. Yang, A new learning algorithm for blind source separation, Advances in Neural Information Processing Systems (NIPS'99), pp.757-763, 1996.

I. [. Abrahamovitz and . Stegun, Handbook of Mathematical Functions, C [Att99] H. Attias : Independent factor analysis, Neural Computation, vol.11, issue.4, pp.803-851, 1972.

. [. Belouchrani, Abed-Meraim : Séparation aveugle au second ordre de sources corrélées, actes du Colloque GRETSI sur le Traitement du Signal et des Images (GRETSI'93), 1993.

J. [. Belouchrani and . Cardoso, Maximum likelihood source separation for discrete sources, proceedings of European Signal Processing Conference (EUSIPCO'94), pp.768-771, 1994.

J. [. Belouchrani and . Cardoso, Maximum likelihood source separation by the expectation-maximization technique : deterministic and stochastic implementation, proceeding of International Symposium on Nonlinear Theory and Application (NOL- TA'95), pp.49-53, 1995.

S. [. Bro and . 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

E. [. Bertrand and . Dufour, La spectroscopie infrarouge et ses applications analytiques. Collection sciences et techniques agroalimentaires, 2000.

O. S. Borgen, N. Davidson, Z. Mingyang, and O. Oyen, The mulivariate n-component resolution problem with minimum assumptions, Mikrochimica Acta, vol.3, issue.11, pp.63-73, 1986.

]. A. Bel95 and . Belouchrani, Séparation autodidacte de sources : algorithmes, performances et applicationsàapplications`applicationsà des signaux expérimentaux, Thèse de doctorat, Ecole Nationale Supérieure des Télécommunications, 1995.

]. O. Ber00 and . Bermond, Méthodes avancées pour la séparation de sources, Thèse de doctorat , Ecole Nationale Supérieure des Télécommunications, 2000.

]. E. Bin03 and . Bingham, Advances in independent component analysis with applications to data mining, Thèse de doctorat, 2003.

[. Bach and M. Jordan, Kernel independent component analysis, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., pp.1-40, 2002.
DOI : 10.1109/ICASSP.2003.1202783

B. [. Borgen and . Kowalski, An extension of the multivariate component-resolution method to three components, Analytica Chimica Acta, vol.174, pp.1-26, 1985.
DOI : 10.1016/S0003-2670(00)84361-5

A. Belouchrani, K. Abed-meraim, and J. Cardoso, A blind source separation technique using second-order statistics, IEEE Transactions on Signal Processing, vol.45, issue.2, pp.434-444, 1997.
DOI : 10.1109/78.554307

D. [. Bonneta and . Nuzillard, Independent component analysis: A new possibility for analysing series of electron energy loss spectra, Ultramicroscopy, vol.102, issue.4, pp.327-337, 2005.
DOI : 10.1016/j.ultramic.2004.11.003

T. [. Bell and . Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

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

A. [. Bro, Centering and scaling in component analysis, Journal of Chemometrics, vol.40, issue.1, pp.16-33, 2003.
DOI : 10.1002/cem.773

P. [. Bierlaire and . Toint, Tuyttens : On iterative algorithms for linear least squares problems with bound constraints. Linear Algebra and Applications, pp.111-143, 1991.

[. Bercher and C. Vignat, Estimating the entropy of a signal with applications
URL : https://hal.archives-ouvertes.fr/hal-00621665

S. [. Cichocki and . Amari, Adaptive blind signal and image processing -Learning algorithms and applications, 2002.

]. Car89 and . Cardoso, Source separation using higher order moments, Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'89), pp.2109-2112, 1989.

]. Car92 and . Cardoso, Iterative techniques for blind source separation using only fourth-order cumulants, Proc. EUSIPCO, pp.739-742, 1992.

]. Car97 and . Cardoso, Infomax and maximum likelihood for source separation, IEEE Signal Processing Letters, vol.4, pp.112-114, 1997.

]. Car98 and . Cardoso, Blind signal separation : statistical principles, Proc. IEEE, pp.2009-2025, 1998.

]. C. Car99b and . Carteret, Etude, par spectroscopie dans le proche infrarouge, et modélisation des structures de surface et de l'hydratation de silices amorphes, Thèse de doctorat, 1999.

]. Car00 and . Cardoso, On the stability of source separation algorithms, Journal of VLSI Signal Processing, vol.26, pp.7-14, 2000.

A. [. Choi, S. Cichocki, and . Amari, Flexible independent component analysis, The Journal of VLSI Signal Processing, vol.26, issue.1/2, pp.25-38, 2000.
DOI : 10.1023/A:1008135131269

J. [. Celeux and . Diebolt, The SEM algorithm : A probabilistic teacher algorithm derived from the EM algorithm for mixture problem, Computational Statistics Quaterly, vol.2, pp.73-82, 1985.

P. [. Cichocki and . Georgiev, Blind separation algorithms with matrix constraints

]. J. Che84 and . Chen, Nonnegative rank factorisation of nonnegative matrices, Linear Algebra and its Applications, vol.62, pp.207-217, 1984.

[. Cardoso and B. H. Laheld, Equivariant adaptive source separation, IEEE Transactions on Signal Processing, vol.44, issue.12, pp.3017-3030, 1996.
DOI : 10.1109/78.553476

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

. [. Cao, Murata : A stable and robust ICA algorithm based on t-distribution and generalized Gaussian distribution models, proceedings of IEEE Workshop on Neural Networks for Signal Processing IX, pp.283-292, 1999.

]. P. Com94 and . Comon, Independent component analysis ? a new concept ? Signal Processing, pp.287-314, 1994.

[. Campbell and G. Poole, Computing nonnegative rank factorizations, Linear Algebra and its Applications, vol.35, issue.21, pp.175-182, 1981.
DOI : 10.1016/0024-3795(81)90272-X

URL : http://doi.org/10.1016/0024-3795(81)90272-x

[. Cardoso and D. Pham, Séparation de sources par l'indépendance et la parcimonie, proceedings 19e colloque GRETSI pour le traitement du signal et des images, 2001.

U. [. Cohen, Nonnegative ranks, decompositions, and factorizations of nonnegative matrices, Linear Algebra and its Applications, vol.190, issue.21, pp.149-168, 1993.
DOI : 10.1016/0024-3795(93)90224-C

[. Cardoso and A. Souloumiac, Blind beamforming for non-gaussian signals, IEE Proceedings F Radar and Signal Processing, vol.140, issue.6, pp.362-370, 1993.
DOI : 10.1049/ip-f-2.1993.0054

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

[. Chen, Q. Shao, and J. G. Ibrahim, Monte Carlo Methods in Bayesian Computation, 2000.
DOI : 10.1007/978-1-4612-1276-8

X. [. Chen and . Wang, A New Approach to Near-Infrared Spectral Data Analysis Using Independent Component Analysis, Journal of Chemical Information and Computer Sciences, vol.41, issue.4, pp.992-1001, 2001.
DOI : 10.1021/ci0004053

]. L. Dev86, Devroy : Non-Uniform Random Variate Generation, 1986.

S. [. Doucet, C. P. Godzill, and . Robert, Marginal maximum a posteriori estimation using Markov chain Monte Carlo, Statistics and Computing, vol.122, issue.42, pp.77-84, 2002.

]. Dje03, Djermoune : Estimation des paramètres de sinuso¨?dessinuso¨?des amorties par décomposition en sous-bandes adaptative. ApplicationàApplication`Applicationà la spectroscopie RMN, Thèse de doctorat, 2003.

M. [. Delyon, E. Lavielle, and . Moulines, Convergence of a stochastic approximation version of the EM algorithm. The Annals of Statistics, pp.94-128, 1999.

N. [. Dempster and D. B. Laird, Rubin : Maximum likelihood from incomplete data via the EM algorithm, Journal of The Royal Statistical Society B, vol.39, issue.1, pp.1-38, 1977.

]. D. Don81 and . Donoho, On minimum entropy deconvolution, Applied Time Series Analysis, pp.565-608, 1981.

V. [. Donoho and . Stodden, When does non-negative matrix factorization give a correct decomposition into parts ?, Proceedings of Advances in Neural Information Processing Systems 16, 2003.

]. A. De-juan, Y. V. Heyden, R. Tauler, and D. L. Massart, Assessment of new constraints applied to the alternating least squares method, Analytica Chimica Acta, vol.346, issue.3, pp.307-318, 1997.
DOI : 10.1016/S0003-2670(97)90069-6

A. [. Dégerine and . Za¨?diza¨?di, Separation of an Instantaneous Mixture of Gaussian Autoregressive Sources by the Exact Maximum Likelihood Approach, IEEE Transactions on Signal Processing, vol.52, issue.6, pp.1499-1512, 2004.
DOI : 10.1109/TSP.2004.827195

]. S. Egu83 and . Egushi, Second order efficiency of minimum contrast estimators in a curved exponential family. The Annals of Statistics, pp.793-803, 1983.

]. S. Egu85 and . Egushi, A differential geometric approach to statistical inference on the basis of contrast functionals, Hiroshima Mathematics Journal, vol.15, pp.341-391, 1985.

S. [. Févotte, P. J. Godsill, and . Wolfe, Bayesian Approach for Blind Separation of Underdetermined Mixtures of Sparse Sources, proceedings of 5th International Conference on Independent Component Analysis and Blind Signal Separation, 2004.
DOI : 10.1007/978-3-540-30110-3_51

. Ghr-+-05-]-p, B. Gizzi, P. Henry, S. Rubini, E. Giroux et al., A multi-approach study of the interaction of the cu(ii) and ni(ii) ions with alanylglycylhistamine, a mimicking pseudopeptide of the serum albumine n-terminal residue, Journal of Inorganic Biochemistry, vol.99, pp.1182-1192, 2005.

]. M. Gl90a and J. Gaeta, Lacoume : Source separation without prior knowledge : The maximum likelihood solution, Proceedings of EUSIPCO, pp.3-5, 1990.

M. Gaeta and J. L. , Lacoume : Estimateurs du maximum de vraisemblancé etendus ` a la séparation de sources non-gaussiennes, pp.419-434, 1990.

[. Guilpin and . Mangeot, Automatic analysis of a Voigt profile or linear combination of several Voigt profiles, Journal of Physics D: Applied Physics, vol.15, issue.4, pp.537-550, 1982.
DOI : 10.1088/0022-3727/15/4/006

]. W. Gre90, . R. Greene-]-w, S. Gilks, and D. J. Richardson, A gamma-distributed stochastic frontier model Spiegehalter : Markov Chain Monte Carlo in Practice, Journal of Econometrics, vol.46, issue.42, pp.141-163, 1990.

A. [. Gelfand, T. M. Smith, and . Lee, Bayesian Analysis of Constrained Parameter and Truncated Data Problems Using Gibbs Sampling, Journal of the American Statistical Association, vol.47, issue.418, pp.523-532, 1984.
DOI : 10.1093/biomet/60.2.319

]. W. Has70 and . Hastings, Monte Carlo sampling methods using Markov chains and their applications, document) [Hay96] S. Haykin : Adaptive Filter Theory, pp.97-109, 1970.

C. [. Hérault and . Jutten, Détection de grandeurs primitives dans un message composite par une architecture de calcul neuromimétique en apprentissage non supervisé

C. [. Hosseini, D. T. Jutten, and . Pham, Markovian source separation, IEEE Transactions on Signal Processing, vol.51, issue.12, pp.3009-3019, 2003.
DOI : 10.1109/TSP.2003.819000

URL : https://hal.archives-ouvertes.fr/hal-00097172

J. [. Hyvärinen, E. Karhunen, and . Oja, Independent Component Analysis, 2001.

E. [. Hyvärinen and . Oja, A Fast Fixed-Point Algorithm for Independent Component Analysis, Neural Computation, vol.9, issue.7, pp.1483-1492, 1997.
DOI : 10.1109/18.212280

]. H. Hot33 and . Hotelling, Analysis of a complex of statistical variables into principal components

J. Edu and . Psy, (document) [Hoy02] P.O. Hoyer : Non-negative sparse coding, Proceedings of IEEE Workshop on Neural Networks for Signal Processing, pp.417-441, 1933.

]. A. Hyv98 and . Hyvärinen, Independent component analysis for time-dependent stochastic processes, Proceedings of International Conference Neural Networks, pp.541-546, 1998.

[. Ida, M. Ando, and H. Toraya, Extended pseudo-Voigt function for approximating the Voigt profile, Journal of Applied Crystallography, vol.33, issue.6, pp.1311-1316, 2000.
DOI : 10.1107/S0021889800010219

J. [. Jutten and . Hérault, Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture, Signal Processing, vol.24, issue.1, pp.1-10, 1991.
DOI : 10.1016/0165-1684(91)90079-X

[. Jiang, Y. Liang, and Y. Ozaki, Principles and methodologies in self-modeling curve resolution, Chemometrics and Intelligent Laboratory Systems, vol.71, issue.1, pp.1-12, 2004.
DOI : 10.1016/j.chemolab.2003.07.002

]. C. Jut87 and . Jutten, Calcul neuromimétique et traitement du signal, Analyse en Composantes Indépendantes, Thèse de doctorat, 1987.

]. T. Kai68 and . Kailath, An innovations approach to least-squares estimation, Part. I : Linear filtering in additive noise, IEEE Transactions on Automatic Control, vol.13, issue.6, pp.646-655, 1968.

]. L. Kau93 and . Kaufman, Maximum likelihood, least squares, and penalized least squares for PET

V. [. Karvanen and . Koivunen, Blind separation methods based on Pearson system and its extensions, Signal Processing, vol.82, issue.4, pp.663-673
DOI : 10.1016/S0165-1684(01)00213-4

]. K. Knu98 and . Knuth, Bayesian source separation and localization Mohammd-Djafari, ´ editeur : proceedings of International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt'98)

]. K. Knu99 and . Knuth, A Bayesian approach to source separation, Proceedings of International Workshop on Independent Component Analysis and Signal Separation (ICA'99), pp.283-288, 1999.

P. [. Karhunen, E. Pajunen, and . Oja, The nonlinear PCA criterion in blind source separation: Relations with other approaches, Neurocomputing, vol.22, issue.1-3, pp.5-20, 1998.
DOI : 10.1016/S0925-2312(98)00046-0

]. S. Kul59 and . Kullback, Information theory and statistics

[. Lee, M. Girolami, and T. Sejnowski, Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources, Neural Computation, vol.28, issue.46, pp.417-441, 1999.
DOI : 10.1109/72.536322

R. [. Lawson and . Hanson, Solving Least-Squares Problems
DOI : 10.1137/1.9781611971217

E. [. Lawton and . Sylvestre, Self Modeling Curve Resolution, Technometrics, vol.9, issue.3, pp.617-633, 1971.
DOI : 10.1214/aoms/1177704248

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

]. D. Mac96 and . Mackay, Maximum likelihood and covariant algorithms for independent component analysis. Internal report

]. E. Mal02 and . Malinowski, Factor Analysis in Chemistry

]. Mar72 and . Markham, Factorizations of non-negative matrices, Proceedings of, pp.45-47, 1972.

]. V. Maz05 and . Mazet, Développement de méthodes de traitements de signaux de spectroscopies : estimation de la ligne de base et du spectre de raies, Thèse de doctorat, pp.6-7, 2005.

]. S. Mbc05a, D. Moussaoui, C. Brie, S. Carteret, D. Moussaoui et al., Non-negative source separation using the maximum likelihood approach Séparation de sources non-négatives par l'approche du maximum de vraisemblance, proceedings of International Workshop on Statistical Signal Processing proceedings of20ì eme colloque GRETSI sur le traitement du signal et des images (GRETSI'2005), 2005.

S. Moussaoui, D. Brie, C. Carteret, and A. Mohammad-djafari, Application of Bayesian Non-negative Source Separation to Mixture Analysis in Spectroscopy, AIP Conference Proceedings, 2004.
DOI : 10.1063/1.1835218

URL : https://hal.archives-ouvertes.fr/hal-00456989

]. V. Mbi05a, D. Mazet, J. Brie, ]. S. Idiermbi05b, D. Moussaoui et al., Simulation of truncated normal variables using several proposal distributions Idier : Non-negative source separation : Range of admissible solutions and conditions for the uniqueness of the solution, proceedings of IEEE International Workshop on Statistical Signal Processing (SSP'2005) proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2005), 2005.

S. Moussaoui, D. Brie, A. Mohammad-djafari, and C. , Carteret : Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling, Accepté pour publication dans IEEE Transactions on Signal Processing, Octobre 2005. (document)

. Mcb-+-05-]-v, C. Mazet, D. Carteret, J. Brie, B. Idier et al., Background removal from spectra by designing and minimising a non-quadratic cost function, pp.121-133, 2005.

C. [. Moussaoui, D. Carteret, A. Brie, and . Mohammad-djafari, Bayesian analysis of spectral mixture data using Markov chain Monte Carlo methods. Accepté pour publication dans Chemometrics and Intelligent Laboratory Systems, Décembre 2005. (document)
URL : https://hal.archives-ouvertes.fr/hal-00022304

J. [. Moulines, E. Cardoso, and . Gassiat, Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997.
DOI : 10.1109/ICASSP.1997.604649

. [. Mohammad-djafari, A Bayesian approach to source separation In proceedings of International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt'99), pp.221-244

T. [. Mclachlan and . Krishnan, The EM algorithm and extensions. series in probability and statistics, 1997.

O. [. Moreau and . Macchi, A one stage algorithm for source separation, International Conference on Acoustics Speech and Signal Processing (ICASSP'1994), pp.49-52, 1994.

D. [. Miskin and . Mackay, Ensemble Learning for blind source separation, Independent Component Analysis : Principles and Practice, pp.209-233, 2001.
DOI : 10.1017/CBO9780511624148.009

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

A. [. Moussaoui, D. Mohammad-djafari, and . Brie, Caspary : A Bayesian method for positive source separation, proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.485-488, 2004.

]. E. Mor01 and . Moreau, A generalization of joint-diagonalization criteria for source separation

]. N. Mrr-+-53, A. Metropolis, M. Rosenbluth, A. Rosenbluth, E. Taller et al., Equation of state calculation by fast computing machines : Separation of a mixture of independent signals using time delayed correlations, Journal of Chemistry Physics Physical Review Letters, vol.21, issue.72, pp.1087-10923634, 1953.

N. [. Moreau, Thirion-Moreau : Nonsymmetrical contrasts for source separation

J. [. Nuzillard, Nuzillard : BSS applied to non-orthogonal signals, editeurs : proceedings of 1st International Workshop on Independent Component Analysis and Blind Signal Separation (ICA'99), pp.25-30

Y. [. Nakamura, S. Suzuki, and . Kobayashi, A method for recovering physiological components from dynamic radionuclide images using the maximum entropy principle: a numerical investigation, IEEE Transactions on Biomedical Engineering, vol.36, issue.9, pp.906-917, 1989.
DOI : 10.1109/10.35299

G. [. Obradovic and . Deco, Information Maximization and Independent Component Analysis: Is There a Difference?, Neural Computation, vol.10, issue.8, pp.2085-2101, 1998.
DOI : 10.1162/neco.1993.5.5.750

]. N. Oht73 and . Ohta, Estimating absorption bands of component dyes by means of principal component analysis, Analytical Chemistry, vol.45, issue.3, pp.553-557, 1973.

M. F. Ochs, R. S. Stoyanova, F. Arias-mendoza, and T. R. , A New Method for Spectral Decomposition Using a Bilinear Bayesian Approach, Journal of Magnetic Resonance, vol.137, issue.1, pp.161-176, 1999.
DOI : 10.1006/jmre.1998.1639

]. P. Paa97 and . Paatero, Least squares formulation of robust non-negative factor analysis. Chemomerics and Intelligent Laboratory Systems, pp.23-35, 1997.

]. J. Pfa73 and . Pfanzagl, Asymptotic expansions related to minimum contrast estimators, The Annals of Statistics, vol.1, pp.993-1026, 1973.

[. Pham and P. Garrat, Blind separation of mixture of independent sources through a quasi-maximum likelihood approach, IEEE Transactions on Signal Processing, vol.45, issue.7, pp.1712-1725, 1997.
DOI : 10.1109/78.599941

URL : https://hal.archives-ouvertes.fr/hal-01485511

[. Pham, P. Garrat, and C. Jutten, Separation of a mixture of independent sources through a maximum likelihood approach, proceedings of European Signal Processing Conference (EUSIPCO'92), pp.771-774, 1992.
URL : https://hal.archives-ouvertes.fr/hal-01485511

]. Pha96 and . Pham, Blind separation of instantaneous mixture sources via an independent component analysis, IEEE Transactions on Signal Processing, vol.44, issue.11, pp.2768-2779, 1996.

]. Pha02 and . Pham, Mutual information approach to blind separation of stationary sources

]. M. Plu02, Plumbley : Conditions for nonnegative independent component analysis, IEEE Signal Processing Letters, vol.9, issue.6, pp.177-180, 2002.

]. M. Plu03, Plumbley : Algorithms for non?negative independent component analysis, IEEE Transactions on Neural Networks, vol.14, issue.3, pp.534-543, 2003.

[. Pesquet and E. Moreau, Cumulant-based independence measures for linear mixtures, IEEE Transactions on Information Theory, vol.47, issue.5, pp.1947-1956, 2001.
DOI : 10.1109/18.930929

URL : https://hal.archives-ouvertes.fr/hal-00621746

E. [. Plumbley and . Oja, A "nonnegative PCA" algorithm for independent component analysis, IEEE Transactions on Neural Networks, vol.15, issue.1, pp.66-76, 2004.
DOI : 10.1109/TNN.2003.820672

L. [. Pearlmutter and . Parra, A context-sensitive generalization of ICA, proceedings of International Conference on Neural Information Processing, 1996.

C. [. Park, R. C. Spiegelman, and . Henry, Bilinear estimation of pollution source profiles and amounts by using multivariate receptor models, Environmetrics, vol.1, issue.7, pp.775-798, 2002.
DOI : 10.1002/env.557

U. [. Paatero and . Tapper, Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, vol.18, issue.2, pp.111-126, 1994.
DOI : 10.1002/env.3170050203

S. [. Press, W. T. Teukolsky, B. P. Vetterling, C. Flannery, J. M. Picci et al., Numerical Recipes in C A partial classification of primes in the positive matrices and in the doubly stochastic matrices, Rapport technique Report BS-R9535, National Research Institute for Mathematics and Computer Science, 1995.

R. [. Roberts and . Choudrey, Data decomposition using independent component analysis with prior constraints, Pattern Recognition, vol.36, issue.8, pp.1813-1825, 2003.
DOI : 10.1016/S0031-3203(03)00002-5

]. C. Rob95, . J. Robertrob98-]-s, and . Roberts, Simulation of a truncated normal variables Independent component analysis : Source assessment and separation, a Bayesian approach, IEE Proceedings on Vision, Image and Signal Processing, pp.121-125, 1995.

]. C. Rob99 and . Robert, Monte Carlo Statistical Methods, 1999.

]. C. Rob01 and . Robert, The Bayesian Choice

]. D. Row98 and . Rowe, Correlated factor analysis, Thèse de doctorat, 1998.

P. [. Sénécal and . Amblard, Bayesian separation of discrete sources via Gibbs sampling, Proceedings of International Conference on Independent Component Analysis and Blind Signal Separation (ICA'2000), pp.566-572, 2000.

E. [. Sitek, G. Di-bella, and . Gullberg, Factor analysis of dynamic structures in dynamic SPECT imaging using maximum entropy, IEEE Transactions on Nuclear Science, vol.46, issue.6, pp.2227-2232, 1999.
DOI : 10.1109/23.819308

A. [. Snoussi and . Djafari, MCMC joint separation and segmentation of hidden Markov fields, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, pp.485-494, 2002.
DOI : 10.1109/NNSP.2002.1030060

URL : https://hal.archives-ouvertes.fr/hal-00119184

. Sdb-+-03-]-p, S. Sajda, T. Du, L. Brown, R. Parra et al., Recovery of constituent spectra in 3D chemical shift imaging using non-negative matrix factorization, Proceedings of International Conference on Independent Component Analysis and Blind Signal Separation (ICA'2003), pp.71-76, 2003.

]. S. Sén02 and . Sénécal, Methodes de simulation Monte Carlo par cha??nescha??nes de Markov pour l'estimation de modèles. Applications en séparation de sources et enégalisationenégalisation, Thèse de doctorat, 2002.

. K. Shk-+-01-]-a, H. C. Smilde, H. A. Hoefsloot, S. Kiers, and H. F. Bijlsma, Boelens : Sufficient condition for unique solutions within a certain class of curve resolution models, Journal of Chemometrics, vol.15, pp.405-411, 2001.

J. [. Snoussi and . Idier, Blind separation of generalized hyperbolic process : unifying approach to stationary non-gaussianity and Gaussian non-stationary, proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2005), Philademphia, 2005.

S. [. Sasaki, S. Kawata, and . Minami, Constrained nonlinear method for estimating component spectra from multicomponent mixtures, Applied Optics, vol.22, issue.22, pp.3599-3606, 1983.
DOI : 10.1364/AO.22.003599

S. [. Sasaki, S. Kawata, and . Minami, Estimation of component spectral curves from unknown mixture spectra, Applied Optics, vol.23, issue.12, pp.1955-1959, 1984.
DOI : 10.1364/AO.23.001955

S. [. Sasaki, S. Kawata, and . Minami, Component analysis of spatial and spectral patterns in multispectral images II Entropy minimization, Journal of the Optical Society of America A, vol.6, issue.1, pp.73-79, 1989.
DOI : 10.1364/JOSAA.6.000073

]. H. Smd01a, A. Snoussi, and . Mohammad-djafari, Penalized maximum likelihood for multivariate gaussian mixtures, Proceedings of the 21st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt'2001), pp.36-46, 2001.

]. H. Smd01b, A. Snoussi, and . Mohammad-djafari, Unsupervised learning for source separation with mixture of Gaussians prior for sources and Gaussian prior for mixture coefficients, Proceedings of IEEE Workshop on Neural Networks for Signal Processing, pp.293-302, 2001.

]. H. Sno03 and . Snoussi, Approche bayésienne en séparation de sources, 2003.

J. [. Stuart and . Ord, Kendall's Advanced Theory of Statistics, 1994.

P. Sajda, S. Du, T. R. Brown, R. Stoyanova, D. C. Shungu et al., Nonnegative Matrix Factorization for Rapid Recovery of Constituent Spectra in Magnetic Resonance Chemical Shift Imaging of the Brain, IEEE Transactions on Medical Imaging, vol.23, issue.12, pp.1453-1465, 2004.
DOI : 10.1109/TMI.2004.834626

]. F. Stvdbm96, J. Cuesta-sanchez, B. Toft, and D. L. Van-den-bogaert, Massart : Orthogonal projection approach applied to peak purity assessment, Analytical Chemistry, vol.68, pp.79-85, 1996.

]. Tho74 and . Thomas, Rank factorizations of nonnegative matrices, SIAM Review, vol.16, issue.21, pp.393-394, 1974.

Y. [. Tong, R. W. Inouye, and . Liu, Waveform-preserving blind estimation of multiple independent sources, IEEE Transactions on Signal Processing, vol.41, issue.7, pp.2461-2470, 1993.
DOI : 10.1109/78.224254

A. [. Tauler, E. Izquierdo-ridorsa, and . Casassas, Simultaneous analysis of several spectroscopic titrations with self-modeling curve resolution, pp.293-300, 1993.

B. [. Tauler, S. Kowalski, and . Fleming, Multivariate curve resolution applied to spectral data from multiple runs of an industrial process, Analytical Chemistry, vol.65, issue.15, pp.2040-2047, 1993.
DOI : 10.1021/ac00063a019

R. [. Tong, V. C. Liu, Y. F. Soon, and . Huang, Indeterminacy and identifiability of blind identification, IEEE Transactions on Circuits and Systems, vol.38, issue.5, pp.499-509, 1991.
DOI : 10.1109/31.76486

]. E. Tsi00 and . Tsionas, Full likelihood inference in normal-gamma stochastic models A [Van97] J.M. Van den Hof : Realization of positive linear systems, document) [VV99] J.M. Van den Hof et J.H. Van Schuppen : Positive matrix factorization via extremal polyhedral cones, pp.183-205287, 1997.

J. [. Windig and . Guilment, Interactive self-modeling mixture analysis, Analytical Chemistry, vol.63, issue.14, pp.1425-1432, 1991.
DOI : 10.1021/ac00014a016

[. Widjaja and M. Garland, Pure component spectral reconstruction from mixture data using SVD, global entropy minimization, and simulated annealing. Numerical investigations of admissible objective functions using a synthetic 7-species data set, Journal of Computational Chemistry, vol.411, issue.9, pp.911-919, 2003.
DOI : 10.1002/jcc.10080

M. [. Wei and . Tanner, A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, vol.51, issue.411, pp.699-704, 1990.
DOI : 10.1214/aos/1176346060

M. [. Yingzhi and . Garland, An improved algorithm for estimating pure component spectra in exploratory chemometric studies based on entropy minimization, Analytica Chimica Acta, vol.359, pp.303-310, 1998.