Statistical Learning

CSE 692
 

 

 

DEPARTMENT OF COMPUTER SCIENCE
STATE UNIVERSITY OF NEW YORK, STONY BROOK

 


 
 
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   Statistical Learning - CSE 692

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Adv. Topics in Statistical Learning: CSE 692

Lectures - Fall 2007

Date  

Slides

 

Assigned Reading

  Presenters
9/6/07   Causality
(pdf, color pdf)
      maov
9/18/07  

Feature/Model Selection:
PCA (Eigenfaces) & ICA
pdf

 
  •  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


  •   maov
    9/20/07   Data Modeling:
    MPCA, TensorFaces
     
  • 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.


  •   maov
    9/25/07   Data Modeling: MICA  
  • 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.


  •   maov
    10/02/2007   Kernel PCA, SVM  
  • 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.


  •   Wei
    10/09/2007   Rank-R Tensor Model (Parafac, Candecomp)  
  • 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)


  •   Jean
    10/16/2007       Work on Project    
    10/23/2007   Linear and Logistic Regression  
  • HTF book: Chapter 3
  • DHS book: Chapter 5 (5.1-5.4)
  •   Michael
    10/30/2007   Probabilistic PCA;
    Non-negative Matrix Factorization
     
  • 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.
  •   Nadim
    11/6/2007   Graphical Models;
    Variational Message Passing
     
  • 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
  •   Wei
    11/13/2007      
  • Paper
  •   Jean
    11/27/2007      
  • Paper
  •   Nadim
    12/04/2007   Project Presentations        
    12/11/2007   Project Presentations