LOW RANK DECOMPOSITIONS FOR INFORMATION RETIEVAL

• 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.
• Using Linear Algebra for Intelligent Information Retrieval. (ut-cs-94-270) Michael W. Berry, Susan T. Dumais, and Gavin W. O'Brien, December 1994. Published in SIAM Review 37:4 (1995), pp. 573-595.
  • A Case Study of Latent Semantic Indexing. (ut-cs-95-271) M.W. Berry, S.T. Dumais, and A.T. Shippy, January 1995.
  • Low-Rank Orthogonal Decompositions for Information Retrieval Applications. (ut-cs-95-284) M.W. Berry and R.D. Fierro, April 1995. Published Numerical Linear Algebra with Applications 3:4 (1996), pp. 301-328.

• A semidiscrete matrix decomposition for latent semantic indexing in information retrieval Tamara G. Kolda and Dianne P, O'Leary, ACM Trans. Information Systems, 16 (1998), pp. 322-346.
  • Computation and uses of the semidiscrete matrix decomposition Tamara G. Kolda and Dianne P, O'Leary,, ACM Trans. Math. Software (to appear), 2000.
  • 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.

• Concept Decompositions for Large Sparse Text Data using Clustering. I.S. Dhillon, D.S. Modha, IBM Research Report RJ 10147, July 8, 1999, submitted for publication. alternate version
  • Class Visualization of High-Dimensional Data with Applications. I.S. Dhillon, D.S. Modha, W.S. Spangler, submitted for publication, 1999. Software is available here.
  • Visualizing Class Structure of Multidimensional Data. I.S. Dhillon, D.S. Modha, W.S. Spangler, Proceedings of the 30th Symposium on the Interface: Computing Science and Statistics, Interface Foundation of North America, vol. 30, pages 488-493, Minneapolis, May, 1998.

• Principal Direction Divisive Partitioning, D. L. Boley, Data Mining and Knowledge Discovery 2(4):325-344 , 1998.
  • Bisecting K-means and PDDP: A Comparative Analysis (Click here, then look for report 00-048 under the year 2000). by Sergio M. Savaresi, Daniel Boley, U of Mn, Computer Science TR 00-048, 2000

• An example-based mapping method for text classification and retrieval. (pdf) Yang, Y., Chute, C.G., ACM Transactions on Information Systems (TOIS) 1994;12(3):252-77.
  • A re-examination of text categorization methods. Yiming Yang and Xin Liu, Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 1999, pp 42--49.
  • An Evaluation of statistical approaches to text categorization. Yiming Yang Journal of Information Retrieval, 1999, Vol 1, No. 1/2, pp 67--88.

• The World's Largest Matrix Computation by Cleve Moler, Matlab News, ``Cleve's Corner'', Oct. 2002.
  • 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.
  • PageRank Computation and the Structure of the Web: Experiments and Algorithms Arvind Arasu, Jasmine Novak, Andrew Tomkins, John Tomlin 11th Intl WWW Conf. poster. May 2002.
  • Authoritative sources in a hyperlinked environment. J. Kleinberg. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. Extended version in Journal of the ACM 46(1999). Also appears as IBM Research Report RJ 10076, May 1997. PDF format available.

• Probabilistic Visual Learning for Object Representation, Baback Moghaddam, Alex Pentland Early Visual Learning, Oxford University Press, 1996.
  • Learning the parts of objects by non-negative matrix factorization. Daniel D. Lee and H. Sebastian Seung Nature 401: 6755, pp 788-792, 21 October 1999.
  • Probabilistic Latent Semantic Indexing , Thomas Hofmann Proceedings of the 22nd International Conference on Research and Development in Information Retrieval (SIGIR'99)

• Support Vector Machines: Hype or Hallelujah? , K. P. Bennett, C. Campbell SIGKDD Explorations, Vol. 2, Issue 2, 2000.
  • Support-Vector Networks. Corinna Cortes 1 and Vladimir Vapnik 2 AT&T Labs-Research, 1995.
  • An overview of statistical learning theory, Vapnik, V.N.; Neural Networks, IEEE Transactions on Volume: 10 Issue: 5, Page(s): 988 -999 Sept. 1999. (this site may be accessible only from local U of M hosts)
  • A Tutorial on Support Vector Machines for Pattern Recognition , C.J.C. Burges, Data Mining and Knowledge Discovery, Vol. 2, Number 2, p. 121-167, 1998 (43 pages)
  • Multiplicative updates for nonnegative quadratic programming in support vector machines F Sha, L Saul, D L Lee, U Penn. TR MS-CIS-02-19, 2002.
  • Input space vs. feature space in kernel-based methods , B. Schölkopf, S. Mika, C.J.C. Burges, P. Knirsch, K.-R. Müller, G. Raetsch and A. Smola, IEEE Transactions on Neural Networks, 1999, 10:5, pp 1000-1017. (19 pages)
  • Distinctive Feature Detection Using Support Vector Machines, P. Niyogi, C.J.C. Burges, and P. Ramesh, Proceedings of the International Conference on Acoustics, Speech and Signal Processing, 1998 (5 pages)
  • Support vector regression and classification based multi-view face detection and recognition., Y. Li, S. Gong and H. Liddell. In Proc. The Fourth IEEE International Conference on Face and Gesture Recognition (FG2000), pages 300-305, Grenoble, France, March 2000

• Wavelet notes; based on: Burris Gopinath Guo Wavelets and Wavelet Transforms. Also available is some Matlab Software (see also the Wavelet toolbox in Matlab).
  
  • Wavelet-Based Image Indexing Techniques with Partial Sketch Retrieval Capability; Wang, James Ze; Gio Wiederhold; Oscar Firschein, and Sha Xin Wei: IEEE Advances in Digital Libraries (ADL-97), Library of Congress, Washington, DC, 7 May 1997, pages 13-24.
    DEMO
  • Content-based image indexing and searching using Daubechies' wavelets, James Z. Wang, Gio Wiederhold, Oscar Firschein, Sha Xin Wei, International Journal of Digital Libraries (IJODL), vol. 1, no. 4, pp. 311-328, Springer-Verlag, 1998.
    DEMO

• An introductory tutorial on kd-trees Extract from A. W. Moore's Phd. thesis: Efficient Memory-based Learning for Robot Control, A. W. Moore, Computer Laboratory, University of Cambridge, Technical Report No. 209, 1991.
  • X-means: Extending K-means with Efficient Estimation of the Number of Clusters, Dan Pelleg and Andrew Moore International Conference on Machine Learning, 2000 (ICML2000)
  • The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data, Andrew W. Moore, Proceedings of UAI-2000: The Sixteenth Conference on Uncertainty in Artificial Intelligence

• CURE: An efficient algorithm for clustering large databases , S. Guha, R. Rastogi and K. Shim, Proceedings of ACM-SIGMOD 1998 International Conference on Management of Data, Seattle, 1998.

• Impact of Similarity Measures on Web-page Clustering, A. Strehl, J. Ghosh and R. Mooney, Proceedings of the 17th National Conference on Artificial Intelligence: Workshop of Artificial Intelligence for Web Search (AAAI 2000), 30-31 July 2000, Austin, Texas, USA, pages 58-64. [Here is A. Strehl's list of publications]

• Scatter/Gather: a cluster-based approach to browsing large document collections Douglass R. Cutting, David R. Karger, Jan O. Pedersen and John W. Tukey Proceedings of the Fifteenth Annual International ACM SIGIR conference on Research and development in information retrieval June 21 - 24, 1992, Copenhagen Denmark pp. 318-329.

• Cluster Analysis, a chapter from
  Electronic Statistics Textbook StatSoft, Inc. (1999), Tulsa, OK: StatSoft.

• Linear Algebra, Jim Hefferon (an elementary-level on-line textbook), 2001.

• Sample lecture notes on Numerical Analysis based on textbook by Michael Heath. In particular, the notes for Chapters 2, 3, 4, are a good review of computational linear algebra.

• Mini tutorial on the Singular Value Decomposition, a section from a Dynamics Tutorial, T Dean, S Leach, H Shatkay; Brown Univ., 1995.

• UCI Machine Learning Repository, Blake, C.L. and Merz, C.J.; University of California - Irvine, Department of Information and Computer Science, 1998.