H. Hotelling, Analysis of a complex of statistical variables into principal components., Journal of Educational Psychology, vol.24, issue.6, p.417, 1933.
DOI : 10.1037/h0071325

A. Bertrand and M. Moonen, Distributed adaptive estimation of covariance matrix eigenvectors in wireless sensor networks with application to distributed PCA, Signal Processing, vol.104, pp.120-135, 2014.
DOI : 10.1016/j.sigpro.2014.03.037

J. Fellus, D. Picard, and P. Gosselin, Dimensionality reduction in decentralized networks by gossip aggregation of principal components analyzers, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.171-176, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00985721

D. Kempe and F. Mcsherry, A decentralized algorithm for spectral analysis, Journal of Computer and System Sciences, vol.74, issue.1, pp.70-83, 2008.
DOI : 10.1016/j.jcss.2007.04.014

A. Wiesel and A. O. Hero, Decomposable Principal Component Analysis, IEEE Transactions on Signal Processing, vol.57, issue.11, pp.4369-4377, 2009.
DOI : 10.1109/TSP.2009.2025806

Z. Meng, A. Wiesel, and A. O. Hero, Distributed principal component analysis on networks via directed graphical models, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2877-2880, 2012.
DOI : 10.1109/ICASSP.2012.6288518

C. Ordonez, N. Mohanam, and C. Garcia-alvarado, Pca for large data sets with parallel data summarization, Distributed and Parallel Databases, pp.377-403, 2014.

P. Bruneau, M. Gelgon, and F. Picarougne, Aggregation of Probabilistic PCA Mixtures with a Variational-Bayes Technique Over Parameters, 2010 20th International Conference on Pattern Recognition, pp.702-705, 2010.
DOI : 10.1109/ICPR.2010.177
URL : https://hal.archives-ouvertes.fr/hal-00471313

M. E. Tipping and C. M. Bishop, Mixtures of Probabilistic Principal Component Analyzers, Neural Computation, vol.2, issue.1, pp.443-482, 1999.
DOI : 10.1007/BF00162527

A. Nikseresht and M. Gelgon, Gossip-Based Computation of a Gaussian Mixture Model for Distributed Multimedia Indexing, IEEE Transactions on Multimedia, vol.10, issue.3, pp.385-392, 2008.
DOI : 10.1109/TMM.2008.917343
URL : https://hal.archives-ouvertes.fr/inria-00368854

A. Nikseresht, Estimation de modèles de mélange probabilistes: une proposition pour un fonctionnement réparti et décentralisé, 2008.

S. V. Macua, P. Belanovic, and S. Zazo, Consensus-based distributed principal component analysis in wireless sensor networks, Signal Processing Advances in Wireless Communications (SPAWC), pp.1-5, 2010.

D. Kempe, A. Dobra, and J. Gehrke, Gossip-based computation of aggregate information, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings., p.482, 2003.
DOI : 10.1109/SFCS.2003.1238221

F. Iutzeler, P. Ciblat, and W. Hachem, Analysis of Sum-Weight-Like Algorithms for Averaging in Wireless Sensor Networks, IEEE Transactions on Signal Processing, vol.61, issue.11
DOI : 10.1109/TSP.2013.2256904
URL : https://hal.archives-ouvertes.fr/hal-00737584

M. Jelasity, W. Kowalczyk, and M. Van-steen, Newscast computing, Tech. rep, 2003.

D. Shah, Gossip Algorithms, Foundations and Trends?? in Networking, vol.3, issue.1, 2009.
DOI : 10.1561/1300000014

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

A. Rhodius, On the maximum of ergodicity coefficients, the Dobrushin ergodicity coefficient, and products of stochastic matrices, Linear Algebra and its Applications, vol.253, issue.1-3, pp.141-154, 1997.
DOI : 10.1016/0024-3795(95)00706-7

E. Seneta, Non-negative matrices and Markov chains, 2006.
DOI : 10.1007/0-387-32792-4