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
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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
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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
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L0, L1, L2, Lp norms as constraints.
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Conjugate and Laplacian matrices.
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Clustering methods.
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Sparse coding principles.
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Basic spectral graph theory (eigenvalues and
eigen subspaces of adjacency matrix for image
classification).
Convex minimization (Gradient descent,
subgradient method, etc.).
Work for this week
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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.