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CSE 704
Seminar in Manifold and Subspace Learning Spring 2010 Instructor: Dr. Yun (Raymond) Fu Course Webpage: http://www.cse.buffalo.edu/~yunfu/course/CSE704FU_Spring2010.htm Times: Wednesday 2pm¡ª4:30pm Location: Bell Hall
224 Office Hours: Right after the
seminar or by appointment Office Hours Location: Bell Hall 241 Course
Overview Designing
subspace learning algorithms using manifold criterion and models is a rapid
emerging area in computer vision and pattern recognition. This seminar will
cover extensive discussions on the state-of-the-art literature in manifold
and subspace learning. Topics, which will be well balanced between the basic
theoretical background and practical applications, include manifold modeling,
dimensionality reduction, discriminant analysis, component analysis,
kernelization, feature extraction/representation, transfer learning,
semi-supervised learning, etc. The involved applications are mainly derived
from the imaging field, such as biometrics, image/video processing, machine
vision, and human-computer interaction. We will read and discuss papers on
the listed topic together. Guest
lecturers will be invited to present some topics if funding is available for
honoraria or expenses. Goals and Grading The default grading is Grading is P/F. Students
will be required to make in-class presentations and lead the discussions. By
special request of letter grading, some students may finish a final project
to study an existing algorithm or invent new algorithms in any related topics.
Note that participation is also considered as a factor for final grading.
Students can be absence for particular reasons (by instructor¡¯s permission). Prerequisites Fundamental knowledge and some experiences of
pattern classification, image processing, and computer vision. Course Topics and Schedules |
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No. |
Date |
Topics
and Papers |
Speaker |
1 |
1/13 |
Introduction |
Raymond |
2 |
1/20 |
Manifold
and Subspace Learning |
Raymond |
3 |
1/27 |
[Roweis and Saul 2000][Yan et al. 2007] |
Kevin & Caiming |
4 |
2/03 |
[Ghahramani et al. 2000][Tenenbaum
et al. 2000] |
Anurag & Kevin |
5 |
2/10 |
[Cetingul
and Vidal 2009][Li et al. 2009] |
Albert &
Timothy |
6 |
2/17 |
[Si et al.
2009] [Xu et al. 2009] |
Ricardo & Anurag |
7 |
2/24 |
[Elhamifar
and Vidal 2009] [Pan et al. 2008] |
Mahesh & Ricardo |
8 |
3/03 |
[Gerber et
al. 2009] |
Albert |
|
3/10 |
Spring Recess -
No Classes |
|
|
3/17 |
ICASSP 2010 - No
Classes |
|
9 |
3/24 |
[Talwalkar et
al. 2008] |
Caiming |
10 |
3/31 |
[Tu et al.
2009] |
Subramanian |
11 |
4/07 |
[Wright et
al. 2009] |
Timothy |
12 |
4/14 |
[Wang et al.
2008] |
Praveen |
13 |
4/21 |
Wrap Up |
All |
|
4/28 |
Reading Days--No class, Projects/reports due |
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Reference
List [01] [Roweis and
Saul 2000] [Yan et al. 2007] Sam Roweis
and Lawrence Saul, ¡°Nonlinear dimensionality reduction by locally linear
embedding,¡± Science, vol. 290, no.
5500, pp. 2323-2326, 2000. S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang,
and S. Lin, ¡°Graph embedding and extensions: A general framework for
dimensionality reduction,¡± IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no.
1, pp. 40¨C51, 2007. [02] [Ghahramani et al. 2000] [Tenenbaum
et al. 2000] Ghahramani, Z. and Beal, M.J., ¡°Variational
Inference for Bayesian Mixtures of Factor Analysers,¡± In Advances in Neural Information Processing Systems, 12:449-455,
MIT Press, 2000. J. B.
Tenenbaum, V. de Silva and J. C. Langford, ¡°A Global Geometric Framework for
Nonlinear Dimensionality Reduction,¡± Science, vol. 290, no. 5500, pp. 2319-2323, 2000. [03]
[Cetingul
and Vidal 2009] [Li et al. 2009] Hasan Ertan
Cetingul and Rene Vidal, ¡°Intrinsic Mean Shift for Clustering on Stiefel and
Grassmann Manifolds,¡± IEEE Conference
on Computer Vision and Pattern Recognition, 2009. R. Li, R.
Chellappa, and S. Kevin Zhou, ¡°Learning Multi-modal densities on
Discriminative Temporal Interaction Manifold for Group Activity Recognition,¡±
IEEE Conference on Computer Vision and
Pattern Recognition, 2009. [04] [Si et al.
2009] [Xu et al. 2009] S. Si, D.
Tao, and B. Geng, ¡°Bregman Divergence Based Regularization for Transfer
Subspace Learning,¡± IEEE Transactions
on Knowledge and Data Engineering, PrePrint, 2009. D. Xu, S.
Yan, S. Lin, Thomas Huang, and Shih-Fu Chang, "Enhancing Bilinear
Subspace Learning by Element Rearrangement," IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 31, no. 10, pp. 1913-1920, 2009. [05] [Elhamifar
and Vidal 2009] [Pan et al. 2008] Ehsan
Elhamifar and Rene Vidal, ¡°Sparse Subspace Clustering,¡± IEEE Conference on Computer Vision and Pattern Recognition, 2009. S. J. Pan, J. T. Kwok, and Q. Yang, ¡°Transfer
learning via dimensionality reduction,¡± in Proceedings of the 23rd AAAI Conference on Artificial Intelligence,
Chicago, Illinois, USA, July 2008, pp. 677¨C682. [06] [Gerber et
al. 2009] Samuel
Gerber, Tolga Tasdizen, and Ross Whitaker, ¡°Dimensionality Reduction and
Principal Surfaces via Kernel Map Manifolds,¡± IEEE International Conference on Computer Vision, 2009. [07] [Talwalkar et
al. 2008] A. Talwalkar,
S. Kumar and H. Rowley, ¡°Large-Scale Manifold Learning,¡± IEEE Conference on Computer Vision and Pattern Recognition, 2008. [08]
[Tu
et al. 2009] Jilin Tu,
Xiaoming Liu, and Peter Tu, ¡°On Optimizing Subspaces for Face Recognition,¡± IEEE International Conference on Computer
Vision, 2009. [09] [Wright et
al. 2009] John Wright, Allen Yang, Arvind Ganesh, Shankar
Sastry, and Yi Ma, ¡°Robust Face Recognition via Sparse Representation,¡± IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 31. no.2, February 2009. [10] [Dacheng et
al. 2007] Dacheng Tao, Xuelong Li, Xindong
Wu, Stephen J. Maybank, "General Tensor Discriminant Analysis and Gabor
Features for Gait Recognition," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 29, no. 10, pp. 1700-1715, June 2007. [11] [Xuelong et
al. 2008] Xuelong Li, Stephen Lin, Shuicheng
Yan, Dong Xu, ¡°Discriminant Locally Linear Embedding With High-Order Tensor
Data,¡± IEEE Transactions on Systems, Man, and Cybernetics, Part B, 342-352,
2008. [12] [Wang et al.
2008] Ruiping Wang,
Shiguang Shan, Xilin Chen, and Wen Gao, ¡°Manifold-Manifold Distance with
Application to Face Recognition based on Image Set,¡± IEEE Conference on Computer Vision and Pattern Recognition, 2008. |
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Last Update: 01-28-2010, Copyright 2004~2010,
Yun Fu, All Rights Reserved |