CSE 705 Seminar in Sparse Representation and Low-Rank Matrix Analytics

Spring 2012

 

NOTE: The full reference list is available as follows, please pick your paper and email me your preferred date for presentation.

 

Instructor: Dr. Yun (Raymond) Fu

Course Webpage: http://www.cse.buffalo.edu/~yunfu/course/CSE705FU_Spring2012.htm

Times: Wednesday 1pm¡ª2pm

Location:  338A Davis Hall 

Office Hours: Right after the seminar or by appointment

TA: Liangyue Li, liangyue@buffalo.edu

Office Hours Location: 331 Davis Hall

 

Course Overview

This is a seminar course covering the popular machine learning topics in sparse representation, low-rank matrix approximation and recovery. We will read and discuss latest papers with all the students involved. 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 machine learning, image processing, and computer vision.

 

Course Topics and Schedules

 

No.

Date

Topics and Papers

Speaker

1

1/18

Introduction

Raymond

2

1/25

Compressed Sensing and Low-Rank Matrix Approximation [1,2]

Kang Li, Wei Chen

3

2/1

Centralized Sparse Representation [3]

Ashutosh Pandey

4

2/8

Image Restoration [7]

Meng Tong

5

2/15

Missing Data [5]

Shuang Wu

6

2/22

Robust Sparse Coding [12]

Devansh Arpit

7

2/29

Tensor Decomposition [13]

Mahmoud Abo Khamis

8

3/7

Randomized Low-rank [10]

Zhi Yang

 

3/14

Spring Recess - No Classes

 

9

3/21

Structured Sparse Representation [4]

Liangyue Li

3/28

ICASSP 2012 - No Classes

10

4/4

Robust Subspace Segmentation [6]

Ming Shao

11

4/11

Super-resolution by Sparse Representation [9]

Dingcheng Ren

12

4/18

Accelerated Low-Rank [8]

Mingbo Ma

13

4/25

Hierarchical Sparse Coding [11]

Jie Hu

 

5/1

Reading Days--No class, Projects/reports due

 

Reference List (FullList)

[01]  Justin Romberg and Michael Wakin, Compressed Sensing: A Tutorial, 2007 www.ee.duke.edu/ssp07/Tutorials/ssp07-cs-tutorial.pdf

[02]  N. Halko, P. G. Martinsson, and J. A. Tropp, Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Rev. 53, pp. 217-288, 2011 http://epubs.siam.org/sirev/resource/1/siread/v53/i2/p217_s1

[03]  W. Dong, L. Zhang, and G. Shi. Centralized sparse representation for image restoration. ICCV, 2011.

[04]  E. Elhamifar and R. Vidal, Robust classification using structured sparse representation. CVPR, 2011.  http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995664&isnumber=5995307

[05]  A. Eriksson and A. van den Hengel. Efficient computation of robust low-rank matrix approximations in the presence of missing data using the l1 norm. CVPR, 2010. http://cs.adelaide.edu.au/~anders/papers/eriksson-cvpr-10.pdf

[06]  G. Liu, Z. Lin, and Y. Yu. Robust subspace segmentation by low-rank representation. In Proceedings of the26th International Conference on Machine Learning (ICML), 2010.

[07]  Haichao Zhang, Jianchao Yang, Yanning Zhang, Nasser M. Nasrabadi and Thomas S. Huang, Close the Loop: Joint Blind Image Restoration and Recognition with Sparse Representation Prior, 13th International Conference on Computer Vision (ICCV), 2011.

[08]  Yadong Mu, Jian Dong, Xiaotong Yuan, and Shuicheng Yan. Accelerated low-rank visual recovery by randomprojection. In Computer Vision and Pattern Recognition (CVPR), 2011.

[09]  J. Yang, J. Wright, T. Huang, and Y. Ma. Image super-resolution as sparse representation of raw image patches. Computer Vision and Pattern Recognition, 2008.

[10]  Tianyi Zhou and Dacheng Tao. Godec: Randomized lowrank & sparse matrix decomposition in noisy case. In ICML, pages 33¨C40, 2011.

[11]  K. Yu, Y. Lin, and J. Lafferty. Learning image representations from the pixel level via hierarchical sparse coding. In Computer Vision and Pattern Recognition (CVPR), 2011

[12]  M. Yang, L. Zhang, J. Yang, and D. Zhang. Robust sparse coding for face recognition. In Computer Visionand Pattern Recognition (CVPR), 2011.

[13]  R. Tomioka, T. Suzuki, K. Hayashi, and H. Kashima. Statistical performance of convex tensor decomposition. Advances in Neural Information Processing Systems (NIPS), page 137, 2011 http://books.nips.cc/papers/files/nips24/NIPS2011_0596.pdf

 

Last Update: 1-8-2012, Copyright 2004~2012, Raymond Fu, All Rights Reserved