Date 
Topic 
Tutorial 
References 
03/16 
Principal Component Analysis
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PCA 
 Max Wellings,
Linear Models.
 Sam Roweis,
EM Algorithms for PCA and SPCA, NIPS 1997.
 Michael Tipping, Christopher Bishop,
Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society, Series, 1999.
 Matthew Turk, Alex Pentland,
Eigenfaces for recognition, Journal of Cognitive Neuroscience, 1991.
 Tim Cootes, C. J. Taylor,
Chapter 4 Statistical Shape Models, from Statistical Models of Appearance for Computer Vision.
 Volker Blanz, Thomas Vetter,
A Morphable Model for the Synthesis of 3D Faces, SIGGRAPH, 1999.
 Brett Allen, Brian Curless, Zoran Popovic,
The Space of Human Body Shapes: Reconstruction and Parameterization from Range Scans, SIGGRAPH, 2003.

03/23 
PCA Extensions
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PCA missing data
Robust PCA 
 Haifeng Chen,
Principal Component Analysis with Missing Data and Outliers.
 Fernando De la Torre, Michael Black,
Robust Principal Component Analysis for Computer Vision, CVPR, 2001.
 Chakra Chennubhotla, Allan Hepson,
SparsePCA Extracting MultiScale Structure from Data, ICCV, 2001.
 Rene Vidal, Yi Ma, Shankar Sastry,
Generalized Principal Component Analysis (GPCA), CVPR, 2003.
 Rene Vidal, Yi Ma, Shankar Sastry,
Algebraic Methods for MultipleSubspace Segmentation.

03/30 
Isomap Locally Linear Embedding
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ISOMAP & LLE 
 Stephen Borgatti,
Multidimensional Scaling.
 Joshua Tenenbaum, Vin de Silva, John Langford,
A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000.
 Sam Roweis, Lawrence Saul,
Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000.
 Lawrence Saul, Sam Roweis,
An Introduction to Locally Linear Embedding.
 Lawrence Saul, Sam Roweis,
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds, Journal of Machine
Learning Research, 2003.
 Robert Pless,
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences, ICCV, 2003.
 Jackie Assa, Yaron Caspi, Daniel CohenOr,
Action synopsis: Pose Selection and Illustration, ICCV, 2003.

04/06 
Laplacian Eigenmaps
Linear Discriminant Analysis
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LDA
LDA applications 
 Mikhail Belkin, Partha Niyogi,
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering, NIPS, 2001.
 Lawrence Saul, Kilian Weinberger, Fei Sha, Jihun Ham, Daniel Lee,
Spectral Methods for Dimensionality Reduction.
 Max Wellings,
Fisher Linear Discriminant Analysis.
 Peter Belhumeur, Joao Hespanha, David Kriegman,
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, PAMI, 1997.
 Jieping Ye, Ravi Janardan, Qi Li,
TwoDimensional Linear Discriminant Analysis, NIPS, 2004.

04/13 
Locality Preserving Projection Local Discriminant Embedding
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LPP 
 Xiaofei He, Partha Niyogi,
Locality Preserving Projection, NIPS, 2003.
 Xiaofei He, Shuicheng Yan, Yuxiao Hu, HongJiang Zhang,
Learning a Locality Preserving Subspace for Visual Recognition, ICCV, 2003.
 Xiaofei He,
Incremental SemiSupervised Subspace Learning for Image Retrieval, ACM Multimedia, 2004.
 Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, HongJiang Zhang,
Face Recognition Using Laplacianfacesl, PAMI, 2005.
 HwannTzong Chen, HuangWei Chang, TyngLuh Liu,
Local Discriminant Embedding and Its Variants, CVPR, 2005.

04/27 05/04 
Support Vector Machines
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SVM
SVM
SVM
libsvm

 Christopher Burges,
A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 1998.
 Max Wellings,
Support Vector Machines.
 Edgar Osuna, Robert Freund, Federico Girosi,
Training Support Vector Machines: an Application to Face Detection, CVPR, 1997.
 Sami Romdhani, Philip Torr, Bernhard Scholkopf, Andrew Blake,
Computationally Efficient Face Detection, ICCV, 2001.
 Shai Avidan,
Support Vector Tracking, PAMI, 2004.
 Apostol Natsev, Milind Naphade, Jelena Tesic,
Learning the Semantics of Multimedia Queries and Concepts from a Small Number of Examples, ACM Multimedia, 2005.


Support Vector Regression
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SVR 


Relevance Vector Machine
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RVM 
 Michael Tipping,
The Relevance Vector Machine, NIPS, 2000.
 Michael Tipping,
Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Lerning Research, 2001.
 Oliver Williams, Andrew Blake, Roberto Cipolla,
Sparse Bayesian Learning for Efficient Visual Tracking, PAMI, 2005.

05/11 
Boosting
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Ensemble Learning
AdaBoost binary
AdaBoost extensions
AdaBoost applications

 Yoav Freund, Robert Schapire,
A DecisionTheoretic eneralization of OnLine Learning and an Application to Boosting,
European Conference on Computational Learning Theory 1995.
 Eric Bauer, Ron Kohavi,
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants,
Machine Learning, 1999.
 Paul Viola, Michael Jones,
Robust RealTime Face Detetion, International Journal of Computer Vision, 2004.
 Paul Viola, Michael Jones,
Detecting Pedestrians Using Patterns of Motion and Appearance, International Journal of Computer Vision, 2005.
 Shai Avidan,
Ensemble Tracking, CVPR, 2005.

05/18 05/25 
Graphical Models
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Graphical Model 


Belief Propagation
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lowlevel learning
Applications 
 Max Wellings,
Belief Popagation.
 Jonathan Yedidia, William Freeman, Yair Weiss,
Understanding Belief Propagation and its Generalizations, IJCAI, 2001.
 Pedro Felzenszwalb, Daniel Huttenlocher,
Efficient Belief Propagation for Early Vision, CVPR, 2004.
 William Freeman, Egon Pasztor, Owen Carmichael,
Learning LowLevel Vision, IJCV, 2001.
 William Freeman, Thouis Jones, Egon Pasztor,
ExampleBased SuperResolution, IEEE CG&A, 2002.
 Jue Wang, Michael Cohen,
An Iterative Optimization Approach for Unified Image Segmentation and Matting, ICCV, 2005.

05/25 06/01 06/08 
Approximate Inference 



Expectation Maximization
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EM 
 Max Wellings,
EMAlgorithm.
 Carlo Tomasi,
Estimating Gaussian Mixture Densities with EM  A Tutorial
 Radford Neal, Geoffrey Hinton,
A View of the EM algorithm that Justifies Incremental, Sparse, and Other Variants.
 Jeff Bilmes,
A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation
for Gaussian Mixture and Hidden Markov Models.
 Frank Dellaert,
The Expectation Maximization Algorithm.
 Yair Weiss,
Motion Segmentation using EM  a Short Tutorial.
 M. Weber, Max Welling, P. Perona,
Unsupervised Learning of Models for Recognition, ECCV 2000.
 M. Weber, Max Welling, P. Perona,
Towards Automatic Discovery of Object Categories, CVPR 2000.


Variational Learning
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Variational Learning
Variational Learning
Variational Learning

 Michael Jordan, Zoubin Ghahramani, Tommis Jaakkola, Lawrence Saul,
An Introduction to Variational Methods for Graphical Models, Machine Learning, 1999.
 Nebojsa Jojic, Brendan Frey,
Learning Flexible Sprites in Video Layers, CVPR, 2001.
 Brendan Frey, Nebojsa Jojic, Anitha Kannan,
Learning Appearance and Transparency Manifolds of Occluded Objects in Layers, CVPR, 2003.
 Nebojsa Jojic, Brendan Frey,
A Generative Model for 2.5D Vision: Estimation Appearance, Transformation, Illumination, Transparency and Occlusion.
 Brendan Frey, Nebojsa Jojic,
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models, PAMI, 2005.
 Li FeiFei, Rob Fergus, Pietro Perona,
A Bayesian Approach to Unsupervised OneShot Learning of Object Categories, PAMI, 2005.
