A Provable Approach for Double-Sparse Coding
Authors: Thanh Nguyen, Raymond Wong, Chinmay Hegde
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we support our analysis via several numerical experiments on simulated data, confirming that our method can indeed be useful in problem sizes encountered in practical applications. |
| Researcher Affiliation | Academia | Thanh V. Nguyen ECE Department Iowa State University thanhng@iastate.edu Raymond K. W. Wong Statistics Department Texas A&M University raywong@tamu.edu Chinmay Hegde ECE Department Iowa State University chinmay@iastate.edu |
| Pseudocode | Yes | Algorithm 1 Truncated Pairwise Reweighting; Algorithm 2 Double-Sparse Coding Descent Algorithm |
| Open Source Code | Yes | Matlab implementation of our algorithms is available online4. 4https://github.com/thanh-isu/double-sparse-coding |
| Open Datasets | No | We generate a synthetic training dataset according to the model described in the Setup. The base dictionary Φ is the identity matrix of size n = 64 and the square synthesis matrix A is a block diagonal matrix with 32 blocks. Each 2x2 block is of form [1 1; 1 1] (i.e., the column sparsity r = 2) . The support of x is drawn uniformly over all 6-dimensional subsets of [m], and the nonzero coefficients are randomly set to 1 with equal probability. |
| Dataset Splits | No | The paper mentions 'disjoint sets P1 and P2 of sizes p1 and p2 respectively' for the initialization stage, but it does not specify explicit training/validation/test splits for the overall experimental evaluation of the model's performance. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab implementation of our algorithms' and refers to 'the implementation of Trainlets' but does not provide specific version numbers for Matlab or any other software libraries/dependencies. |
| Experiment Setup | Yes | For all the approaches except Trainlets, we use T = 2000 iterations for the initialization procedure, and set the number of steps in the descent stage to 25. ... The learning step of Trainlets is executed for 50 iterations. |