The goal of the seminar is to study different methods for non-negative matrix factorization, as well as its applications to machine learning, information retrieval and recommender systems. In each session, two different papers will be presented by students. The evaluation of the course will be exclusively based on the presentation of the papers.
Technical University of Denmark. NMF:DTUToolbox http://isp.imm.dtu.dk/toolbox/nmf/
[S. Bucak and B. Gunsel, 2009] S. Bucak and B. Gunsel, "Incremental subspace learning via non-negative matrix factorization," Pattern Recognition, vol. 42, no. 5, pp. 788-797, May 2009. [Online]. Available: http://dx.doi.org/10.1016/j.patcog.2008.09.002
[Ding, He and Simon, 2005] Ding, C., X. He, and H. D. Simon. 2005. On the equivalence of nonnegative matrix factorization and spectral clustering. In Proc. SIAM Data Mining Conf, 606–610.
[Ding, Li and Jordan, 2010] Ding, C., T. Li, and M. I. Jordan. 2010. Convex and semi-nonnegative matrix factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(1):45–55.
[Ding, Li and Peng, 2006] Ding, C., T. Li, and W. Peng. 2006. Nonnegative matrix factorization and probabilistic latent semantic indexing: equivalence, chi-square statistic, and a hybrid method. In AAAI’06: Proceedings of the 21st national conference on Artificial intelligence held in Boston, Massachusetts, 342–347. AAAI Press.
[Gaussier and Goutte, 2005] Gaussier, E., and C. Goutte. 2005. Relation between plsa and nmf and implications. In SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval held in Salvador, Brazil, 601–602. ACM.
[Koren, Bell and Volinsky, 2009] Koren, Y., R. Bell, and C. Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42(8):30–37.
[Lee and Seung, 1999] Lee, D. D., and H. S. Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791
[Lee and Seung, 2001] Lee, D. D., and H. S. Seung. 2001. Algorithms for non-negative matrix factorization. Advances in neural information processing systems:556–562
[Takács et al., 2009] Takács, G., I. Pilászy, B. Németh, and D. Tikk. 2009. Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10:623–656
[Xu, Liu and Gong, 2003] Xu, W., X. Liu, and Y. Gong. 2003. Document clustering based on non-negative matrix factorization. In SIGIR ’03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 267–273. ACM.
[Zass and Shashua, 2005] Zass, R., and A. Shashua. 2005. A unifying approach to hard and probabilistic clustering. In ICCV ’05: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1, 294–301. IEEE Computer Society
[Zhang, Zhou and Chen, 2006] Zhang, D., Z.-H. Zhou, and S. Chen. 2006. Non-negative matrix factorization on kernels. 404–412.
[Taylan, 2009] Ali Taylan Cemgil, Bayesian Inference for Nonnegative Matrix Factorisation Models, Computational Intelligence and Neuroscience, vol. 2009, Article ID 785152, 17 pages, 2009.
[Boutsidis, 2008] C. Boutsidis and E. Gallopoulos, SVD based initialization: A head start for nonnegative matrix factorization, Pattern Recognition. Volume 41, Issue 4, April 2008, Pages 1350-1362.
Matlab NMF Implementation