D. Blei, A. Ng, and M. Jordan, Latent Dirichlet Allocation, J. of Machine Learning Research, vol.3, issue.4, pp.993-1022, 2003.

K. Chaudhuri, S. Kakade, K. Liescu, and K. Shridharan, Multi-view clustering via canonical correlation analysis, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553391
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.2973

A. Cheriyadat and R. Radke, Non-negative matrix factorization of partial track data for motion segmentation, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459311

C. Ding, T. Li, and W. Peng, NMF and PLSI, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '06, 2006.
DOI : 10.1145/1148170.1148295

D. Donoho and V. Stodden, When Does Nonnegative Matrix Factorization Give a Correct Decomposition into Parts?, Proc. NIPS, 2004.

K. Fukumizu, F. Bach, and A. Gretton, Statistical Consistency of Kernel Canonical Correlation Analysis, J. of Machine Learning Research, vol.8, issue.2 1, pp.361-383, 2007.

E. Gaussier and C. Goutte, Relation between PLSA and NMF and implications, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '05, 2005.
DOI : 10.1145/1076034.1076148

G. Golub and J. Van-loan, Matrix Computations, 1996.

D. Guillamet, J. Vitria, and B. Schiele, Introducing a weighted non-negative matrix factorization for image classification, Pattern Recognition Letters, vol.24, issue.14, pp.2447-2454, 2003.
DOI : 10.1016/S0167-8655(03)00089-8

D. Hardoon and J. Shaw-taylor, Convergence analysis of kernel Canonical Correlation Analysis: theory and practice, Machine Learning, pp.23-38, 2009.
DOI : 10.1007/s10994-008-5085-3

H. Hotelling, Relations Between Two Sets of Variates, Biometrika, vol.28, issue.1, pp.3-4321, 1936.

I. Jolliffe, Principal Component Analysis, 1986.
DOI : 10.1007/978-1-4757-1904-8

B. Klingenberg, J. Curry, and A. Dougherty, Non-negative matrix factorization: Ill-posedness and a geometric algorithm, Pattern Recognition, vol.42, issue.5, pp.918-928, 2008.
DOI : 10.1016/j.patcog.2008.08.026

A. Langville, C. Meyer, and R. Albright, Initializations for the Nonnegative Matrix Factorization, Proc. ACM KDD, 2006.

D. Lee and H. Seung, Algorithms for Non-negative Matrix Factorization, Proc. NIPS, p.3, 2000.

D. D. Lee and H. S. Seung, Learning the Parts of Objects by Non-negative Matrix Factorization, Nature, vol.401, issue.2, pp.788-799, 1999.

D. Mackay and L. Peto, A hierarchical Dirichlet language model, Natural Language Engineering, vol.19, issue.03, pp.1-19, 1995.
DOI : 10.1016/0168-9002(94)00931-7

G. Salton and M. J. Mcgill, Introduction to Modern Information Retrieval, 1983.

E. Shechtman and M. Irani, Matching local selfsimilarities across images and videos, IEEE Conference on Computer Vision and Pattern Recognition 2007 (CVPR'07), 2005.

W. Tang, Z. Lu, and I. Dhillon, Clustering with Multiple Graphs, 2009 Ninth IEEE International Conference on Data Mining, 2009.
DOI : 10.1109/ICDM.2009.125

C. Thurau and V. Hlavac, Pose primitive based human action recognition in videos or still images, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587721

C. Thurau, K. Kersting, and C. Bauckhage, Convex Non-negative Matrix Factorization in the Wild, 2009 Ninth IEEE International Conference on Data Mining, 2009.
DOI : 10.1109/ICDM.2009.55

C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 1998.
DOI : 10.1109/ICCV.1998.710815
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.2091

N. Vasiloglou, A. Gray, and D. Anderson, Non-Negative Matrix Factorization, Convexity and Isometry, Proc. SIAM DM, 2009.
DOI : 10.1137/1.9781611972795.58
URL : http://arxiv.org/abs/0810.2311

R. Zass and A. Shashua, A unifying approach to hard and probabilistic clustering, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.27

D. Zhou and C. Burges, Spectral clustering and transductive learning with multiple views, Proceedings of the 24th international conference on Machine learning, ICML '07, 2007.
DOI : 10.1145/1273496.1273642