Week 1

Progress Report #2
Alvaro Velasquez
Project Selection
I chose to work with Nasim Souly on the project
titled “Subspace Clustering via Graph Regularized
Sparse Coding”. I chose this topic because the
mathematical aspect of it interested me and I
believe that sparse coding in general is useful to
many fields in computer science and possibly
graph theory.
Papers Read
Sparse Subspace Clustering via Group Sparse
Coding. - Saha et al.
Graph Regularized Sparse Coding for Image
Representation. - Zheng et al.
Least Squares Optimization with L1-Norm
Regularization. - Mark Schmidt
Robust Face Recognition via Sparse
Representation. - Wright et al.
Papers Read
A Discrete Chain Graph Model for 3d+t Cell
Tracking with High Misdetection Robustness.
- Kausler et al.
Evaluation of Super-Voxel Methods for Early
Video Processing.
- Xu et al.
Spectral Clustering of Linear Subspaces for
Motion Segmentation.
- Lauer et al.
Graph Regularized Nonnegative Matrix
Factorization for Data Representation.
- Cai et al.
Topics learned
L0, L1, L2, Lp norms as constraints.
Conjugate and Laplacian matrices.
Clustering methods.
Sparse coding principles.
Basic spectral graph theory (eigenvalues and
eigen subspaces of adjacency matrix for image
Convex minimization (Gradient descent,
subgradient method, etc.).
Work for this week
I will be trying to implement the first steps of
video segmentation using sparse coding (no
graph regularization yet).
To achieve this, I will solve the minimization
problem ||y – DX||2 + lambda||X||1.
Y is an image patch, D is the dictionary, and X
is the coefficient matrix to be made sparse via
L1 minimization.
I will test my solution on the SegTrack data set.