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Adv. Topics in Statistical Learning: CSE 692
Lectures - Fall 2007
Date |
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Slides |
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Assigned Reading |
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Presenters |
9/6/07 |
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Causality (pdf, color pdf) |
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maov |
9/18/07 |
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Feature/Model Selection: PCA
(Eigenfaces) & ICA pdf |
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M. Kirby and L. Sirovich, “Application of the
Karhunen-Loeve procedure for the characterization of
human faces”, 1990
Turk, M. & Pentland, A. (1991). "Eigenfaces for recognition" Journal of Cognitive Neuroscience, 3, 71-86.
Bartlett M.S., Movellan J.R., Sejnowski T.J, `Face
Recognition by Independent Component Analysis', IEEE
Transactions on Neural Networks 13 (6) (2002) 1450-
1464.
Bishop: Sect. 8.6 and Appendix E
DHS: Sect. 10.13
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maov |
9/20/07 |
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Data Modeling: MPCA,
TensorFaces |
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Vasilescu, M.A.O., Terzopoulos, D., "Multilinear Analysis of Image Ensembles: TensorFaces," Proc. 7th European Conference on
Computer Vision (ECCV'02), Copenhagen, Denmark, May, 2002, in Computer
Vision -- ECCV 2002, Lecture Notes in Computer Science, Vol. 2350,
A. Heyden et al. (Eds.), Springer-Verlag, Berlin, 2002, 447-460.
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maov |
9/25/07 |
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Data Modeling:
MICA |
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Vasilescu, M.A.O., Terzopoulos, D., `Multilinear Independent Components Analysis', Proc. Computer Vision and Pattern Recognition Conf. (CVPR '05), 547-553 vol.1, 2005.
Hyvarinen, Aapo "Independent Component Analysis: Algorithms and Applications" in Neural Networks, 13(4-5):411-430, 2000.
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maov |
10/02/2007 |
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Kernel PCA, SVM |
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Bernhard Scholkopf, Alexander Smola and Klaus-Robert Muller
"Nonlinear Component Analysis as a Kernel Eigenvalue Problem", Neural Computation, Vol 10, 1299-1319
K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf. "An introduction to kernel-based learning algorithms," IEEE Neural Networks, 12(2):181-201, May 2001.
C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998
Tutorials on Kernel Methods and SVM
V. Vapnik, "The Nature of Statistical Learning Theory" Springer Verlag, New York 1995.
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Wei |
10/09/2007 |
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Rank-R Tensor Model (Parafac,
Candecomp) |
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R. Bro, PARAFAC: Tutorial & applications, in: 2nd Internet Conf. in Chemometrics (INCINC'96), Chemometrics Intell. Lab. Syst., vol. 38, 1997, pp. 149–171 (special issue)
de Almeida, A. L., Favier, G., and Mota, J. C. 2007. PARAFAC-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization. Signal Process. 87, 2 (Feb. 2007), 337-351
R.A. Harshman, Foundations of the PARAFAC procedure: model and conditions for an “explanatory” multi-mode factor analysis, UCLA Working Papers in Phonetics 16(1) (1970)
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Jean |
10/16/2007 |
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Work on Project |
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10/23/2007 |
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Linear and Logistic
Regression |
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HTF book: Chapter 3
DHS book: Chapter 5 (5.1-5.4)
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Michael |
10/30/2007 |
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Probabilistic PCA;
Non-negative Matrix Factorization |
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B. Moghaddam, W. Wahid, and A. Pentland "Beyond eigenfaces: Probabilistic matching for face recognition", In Proc. of International Conf. on Automatic Face and Gesture Recognition, pages 30--35, Nara, Japan, April 1998.
Lee, D.D.; Seung, H.S. Learning the parts of objects by non-negative matrix factorization.
Nature, vol.401, no.6755, Macmillan Magazines, 21 Oct. 1999. p.788-91.
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Nadim |
11/6/2007 |
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Graphical Models;
Variational Message Passing |
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Murphy, Kevin
"An Introduction to Graphical Models"Technical report, Intel Research Technical Report., 2001.
Winn, J. and Bishop, C. (2005). Variational message passing. Journal of Machine Learning Research. 6:661-694
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Wei |
11/13/2007 |
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Paper
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Jean |
11/27/2007 |
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Paper |
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Nadim |
12/04/2007 |
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Project Presentations |
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12/11/2007 |
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Project Presentations |
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