计算机视觉中——流形学习

今日对于流形学习，不知如何下手才好~

发现一个关于Seminar in Manifold and Subspace Learning课程网址 http://www.cse.buffalo.edu/~yunfu/course/CSE704FU_Spring2010.htm

系统安排了相关学习文献，觉得甚好~

转载于此~

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—4:30pm 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
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
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. 40–51, 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. 677–682.   [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.   参考网址：http://www.cse.buffalo.edu/~yunfu/course/CSE704FU_Spring2010.htm