Course Description

Methodology
  • The course is based on a reading material set that is mainly composed of research papers and book  chapters.
  • Each paper/chapter is assigned to a session. All the students are supposed to read it. The presenter is randomly chosen.
Contents

 Session Reading      Resources
06/03/2012 [Deerwester90] Indexing by latent semantic analysis
13/03/2012  [Park09] An analysis of latent semantic term self-correlation  
20/03/2012  [Hofmann99] Probabilistic latent semantic analysis 
27/03/2012  [Ding06] Nonnegative matrix factorization and probabilistic latent semantic indexing: equivalence, chi-square statistic, and a hybrid method   
10/04/2012  [Blei03]  Latent Dirichlet Allocation  presentación
17/04/2012  [Agarwal10] fLDA: matrix factorization through latent dirichlet allocation  presentación
http://videolectures.net/wsdm2010_chen_fmftl/
24/04/2012 [Pereira11] Generating Text from Functional Brain Images  presentación
05/06/2012[Koren2009] Matrix factorization techniques for recommender systemspresentación
19/06/2012[Cichocki2009]  Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation
1.4 Tensor Properties and Basis of Tensor Algebra
1.5 Tensor Decompositions and Factorizations
1.5.1 Why Multi-way Array Decompositions and Factorizations?
1.5.2 PARAFAC and Nonnegative Tensor Factorization
 presentación


Bibliography


[Agarwal10] Agarwal D, Chen B-chung. "fLDA: Matrix Factorization through Latent Dirichlet Allocation" Proceedings of the third ACM international conference on Web search and data mining - WSDM  ’10. New York, New York, USA: ACM Press; 2010 [cited 2012 Mar 13]. p. 91–100.

[Blei03] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, vol. 3, no. 4-5, pp. 993-1022, May 2003. 

[Cichocki2009] Cichocki, Andrzej et al. Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. Wiley, 2009.http://www.bsp.brain.riken.jp/~cia/NMF_NTF_book/NMF-NTF-book-Chapter1_2-contents.pdf

[Deerwester90] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis,” Journal of the American society for information science, vol. 41, no. 6, pp. 391–407, 1990. 
Available from: http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf

[Ding06] C. Ding, T. Li, and W. Peng, “Nonnegative matrix factorization and probabilistic latent semantic indexing: equivalence, chi-square statistic, and a hybrid method” in Proceedings Of The National Conference On Artificial Intelligence, 2006, pp. 342-347.

[Hofmann99] T. Hofmann, “Probabilistic latent semantic analysis,” Proc. of Uncertainty in Artificial Intelligence, UAI’ 99, pp. 21-28, 1999. 
Available from: http://www.cs.brown.edu/people/th/papers/Hofmann-UAI99.pshttp://www.csail.mit.edu/~jrennie/trg/papers/hofmann-uai99.ps.gz

[Koren2009] [Koren2009] Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 42.8 (2009): 30-37. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.8295

[Park09] L. A. F. Park and K. Ramamohanarao, “An analysis of latent semantic term self-correlation,” ACM Transactions on Information Systems (TOIS), vol. 27, no. 2, pp. 1–35, Feb. 2009. Available from: http://portal.acm.org/citation.cfm?doid=1462198.1462200

[Pereira11] F. Pereira, G. Detre, and M. Botvinick, “Generating Text from Functional Brain Images,” Frontiers in Human Neuroscience, vol. 5, no. August, pp. 1-11, 2011. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3159951