Tentative early list of papers to be presented during the semester.
General x Linear Algebra in Data Exploration - Examples Basics of Eigenvalues, PCA definition x A tutorial on Principal Components Analysis Lindsey I Smith http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf x A Tutorial on Principal Component Analysis Jonathon Shlens http://www.snl.salk.edu/~shlens/pub/notes/pca.pdf (also "http://www.dgp.toronto.edu/~aranjan/tuts/pca.pdf") Information retrieval and clustering using vector-based similarities x Computational Methods for Intelligent Information Access. M.W. Berry, S.T. Dumais, and T.A. Letsche.Proceedings of Supercomputing'95, San Diego, CA, December 1995. http://citeseer.ist.psu.edu/438263.html http://hpc.isti.cnr.it/~palmeri/datam/articles/SC95.ps x Latent semantic indexing via a semi-discrete matrix decomposition Tamara G. Kolda and Dianne P, O'Leary, in The Mathematics of Information Coding, Extraction and Distribution, G. Cybenko et al., eds., vol. 107 of IMA Volumes in Mathematics and Its Applications. Springer-Verlag, 1999, pp. 73-80. http://portal.acm.org/citation.cfm?id=291131 x Computation and Uses of the Semidiscrete Matrix Decomposition (1999) Tamara G. Kolda, Dianne P. O'Leary http://citeseer.ist.psu.edu/173664.html x Outlier Detection Using SemiDiscrete Decomposition (2002) S. McConnell, D. Skillicorn http://citeseer.ist.psu.edu/mcconnell02outlier.html x Concept Decompositions for Large Sparse Text Data using Clustering. I.S. Dhillon, D.S. Modha, IBM Research Report RJ 10147, July 8, 1999, Machine Learning, 42:1, pages 143-175, January 2001. http://www.cs.utexas.edu/users/inderjit/public_papers/concept_mlj.pdf x Streaming Data Reduction Using Low-Memory Factored Representations. David Littau and Daniel Boley. Journal of Information Sciences, 176(14):2016-2041, Elsevier, 2006. http://www-users.cs.umn.edu/~boley/publications/papers/Streaming05.pdf x Video Google: A Text Retrieval Approach to Object Matching in Videos Sivic, J. and Zisserman, A. Proceedings of the International Conference on Computer Vision (2003) http://www.robots.ox.ac.uk/~vgg/publications/papers/sivic03.pdf Demo: http://www.robots.ox.ac.uk/~vgg/research/vgoogle/index.html Slides: https://www.ipam.ucla.edu/publications/sews2/sews2_7272.pdf x Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication Fast Monte Carlo Algorithms for Matrices II: Computing a Low Rank Approximation to a Matrix P. Drineas, R. Kannan, and M.W. Mahoney SIAM J Computing 2005 I: http://www.cs.rpi.edu/~drinep/matrixI_SICOMP.pdf II: http://www.cs.rpi.edu/~drinep/matrixII_SICOMP.pdf Related Slides: http://www.cs.rpi.edu/~drinep/SDMtutorial.ppt xx 10/02 on travel SVD/PCA in Biology -- Microarray analysis. x Singular value decomposition for genome-wide expression data processing and modeling Orly Alter, Patrick O. Brown, David Botstein Proc Natl Acad Sci U S A. 2000 August 29; 97(18): 10101-10106. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=27718 x Example of how SVD/PCA are used to analyse data - using example from Biology Singular value decomposition and principal component analysis. Michael E. Wall http://public.lanl.gov/mewall/kluwer2002.html x Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms Orly Alter, Patrick O. Brown, and David Botstein PNAS, March 18, 2003, vol. 100, no. 6, pp 3351-3356 http://www.pnas.org/cgi/content/abstract/100/6/3351 Linear discrinimant Analsys (LDA) x Fisher Linear Discriminant Analysis Max Wellman http://www.cs.huji.ac.il/~csip/Fisher-LDA.pdf x Developmental Stage Annotation of Drosophila Gene Expression Pattern Images via an Entire Solution Path for LDA Jieping Ye, Jianhui Chen, Ravi Janardan, Sudhir Kumar http://doi.acm.org/10.1145/1342320.1342324 Multidimensional Scaling x Multidimensional Scaling http://www.stat.psu.edu/~chiaro/BioinfoII/mds_sph.pdf x Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis J B Kruskal Psychometrika Volume 29, Number 1 / March, 1964 http://www.springerlink.com/content/010q1x323915712x/ x Latent Semantic Indexing is an Optimal Special Case of Multidimensional Scaling Brian T. Bartell, Garrison W. Cottrell, Richard K. Belew http://doi.acm.org/10.1145/133160.133191 x Data visualization by multidimensional scaling: a deterministic annealing approach. Hansjoerg Klock and Joachim M. Buhmann Pattern Recognition Volume 33, Issue 4, April 2000, Pages 651-669 http://dx.doi.org/10.1016/S0031-3203(99)00078-3 http://www.springerlink.com/content/010q1x323915712x x MULTIDIMENSIONAL SCALING Forrest W. Young http://forrest.psych.unc.edu/teaching/p208a/mds/mds.html x Multidimensional Scaling Stephen P. Borgatti http://www.analytictech.com/borgatti/mds.htm ISOMAP, Local Linear Embedding and visualization x A Global Geometric Framework for Nonlinear Dimensionality Reduction Joshua B. Tenenbaum, Vin de Silva, and John C. Langford Science 22 December 2000: 2319-2323. http://www.sciencemag.org/cgi/reprint/290/5500/2319.pdf (alternate source) http://web.mit.edu/cocosci/Papers/sci_reprint.pdf x Nonlinear Dimensionality Reduction by Locally Linear Embedding Sam T. Roweis and Lawrence K. Saul Science 22 December 2000: 2323-2326. x An Introduction to Locally Linear Embedding. Lawrence Saul & Sam Roweis. [draft version (Jan.01)] http://www.cs.toronto.edu/%7Eroweis/lle/papers/lleintro.pdf (also "http://www.cs.toronto.edu/~roweis/lle/publications.html") Non-Negative Matrix Factorization x Algorithms for Non-Negative Matrix Factorization. David Lee & H Sebastian Seung. http://hebb.mit.edu/people/seung/papers/nmfconverge.pdf x Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares for Microarray Data Analysis, H Kim and H Park Bioinformatics, 23-12:1495-1502, 2007. http://www.cc.gatech.edu/~hpark/papers/kp07snmf.pdf Link Analysis -- PageRank. HITS x The PageRank Citation Ranking: Bringing Order to the Web.Page, Lawrence; Brin, Sergey; Motwani, Rajeev; Winograd, Terry. Stanford Univ. Computer Science Dept technical report. Oct. 2001 http://citeseer.ist.psu.edu/page98pagerank.html x Link Analysis, Eigenvectors and Stability, Andrew Y. Ng and Alice X. Zheng and Michael I. Jordan. IJCAI 2001, p 903-910. http://citeseer.ist.psu.edu/ng01link.html x Google's PageRank Explained and how to make the most of it Phil Craven (web page only) http://www.webworkshop.net/pagerank.html x Inside PageRank Monica Bianchini, Marco Gori, Franco Scarselli. http://portal.acm.org/citation.cfm?doid=1052934.1052938 x Deeper Inside PageRank Amy N. Langville and Carl D. Meyer Internet Mathematics Vol. 1, No. 3: 335-380 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.im/1109190965 http://projecteuclid.org/euclid.im/1109190965 x Authoritative Sources in a Hyperlinked Environment Jon M. Kleinberg Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. http://www.cs.cornell.edu/home/kleinber/auth.pdf Eigenfaces x Probabilistic Visual Learning for Object Representation, Baback Moghaddam, Alex Pentland Early Visual Learning, Oxford University Press, 1996. http://citeseer.ist.psu.edu/moghaddam96probabilistic.html x View based and modular eigenspaces for face recognition. A Pentland, B Moghaddam, T Starner. IEEE Conf on Computer Vision & Pattern Recognition, Seattle, June 1994. http://ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=323814. Alternate paper (with the same figures, intact): http://citeseer.ist.psu.edu/moghaddam94face.html x Eigenfaces for recognition. M. Turk and A. Pentland (1991). Journal of Cognitive Neuroscience 3 (1): 71-86. http://www.cs.ucsb.edu/~mturk/Papers/jcn.pdf x Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. P. Belhumeur, J. Hespanha, and D. Kriegman (july 1997). IEEE Transactions on pattern analysis and machine intelligence 19 (7). http://dx.doi.org/10.1109/34.598228 Spectral Clustering x A Tutorial on Spectral Clustering Ulrike von Luxburg http://web.mit.edu/~wingated/www/introductions/tutorial_on_spectral_clustering.pdf x On Spectral Clustering: Analysis and an algorithm Andrew Y. Ng, Michael I. Jordan, Yair Weiss http://citeseer.ist.psu.edu/ng01spectral.html x Principal Direction Divisive Partitioning D. Boley Data Mining and Knowledge Discovery, 2(4):325-344, Dec. 1998. http://www-users.cs.umn.edu/~boley/publications/papers/PDDP.pdf Spectral Graph Partitioning x Spectral Graph Partitioning David Gleich, Leonid Zhukov, Kevin Lang Slides: http://www.gg.caltech.edu/~zhukov/papers/Spectral_Graph_Partitioning.pdf x Co-clustering documents and words using Bipartite Spectral Graph Partitioning Inderjit S. Dhillon http://www.cs.utexas.edu/users/inderjit/public_papers/kdd_bipartite.pdf (Co-clustering topic in this paper discussed subsequently.) x On the Performance of Spectral Graph Partitioning Methods Stephen Guattery, Gary L. Miller {SODA}: {ACM}-{SIAM} Symposium on Discrete Algorithms (A Conference on Theoretical and Experimental Analysis of Discrete Algorithms) 1995 http://citeseer.ist.psu.edu/148697.html co-clustering x Information-Theoretic Coclustering Inderjit S. Dhillon, Subramanyam Mallela, Dharmendra S. Modha KDD 2003 http://www.cs.utexas.edu/users/inderjit/public_papers/kdd_cocluster.pdf Related Slides: http://www.ima.umn.edu/talks/workshops/5-6-9.2003/dhillon/dhillon.ppt Related Slides: http://www.cs.utexas.edu/users/inderjit/Talks/InfoTheoryCoClust.ppt Support Vector Machines x Support Vector Machines: Hype or Hallelujah?, K. P. Bennett, C. Campbell SIGKDD Explorations, Vol. 2, Issue 2, 2000. http://www.acm.org/sigs/sigkdd/explorations/issue2-2/bennett.pdf