Fast greedy algorithms for dictionary selection with generalized sparsity constraints
Authors: Kaito Fujii, Tasuku Soma
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using numerical experiments, we show that our algorithm outperforms the known methods for dictionary selection, achieving competitive performances with dictionary learning algorithms in a smaller running time. |
| Researcher Affiliation | Academia | Kaito Fujii Graduate School of Information Sciences and Technology The University of Tokyo kaito_fujii@mist.i.u-tokyo.ac.jp Tasuku Soma Graduate School of Information Sciences and Technology The University of Tokyo tasuku_soma@mist.i.u-tokyo.ac.jp |
| Pseudocode | Yes | Algorithm 1 Replacement Greedy & Replacement OMP |
| Open Source Code | No | The paper states 'All the algorithms are implemented in Python 3.6.' and that they implemented their proposed and existing methods, but it does not provide an explicit statement about releasing their code or a link to a code repository. |
| Open Datasets | Yes | The second one is a dataset of real-world images extracted from PASCAL VOC2006 image datasets [15]. |
| Dataset Splits | No | The paper mentions 'We make datasets for training and test in the same way, and use the training dataset for obtaining a dictionary and the test dataset for measuring the quality of the output dictionary.' but does not specify exact percentages, sample counts, or the methodology for these splits (e.g., random, stratified, or predefined splits with citations). |
| Hardware Specification | Yes | We conduct the experiments in a machine with Intel Xeon E3-1225 V2 (3.20 GHz and 4 cores) and 16 GB RAM. |
| Software Dependencies | No | The paper states 'All the algorithms are implemented in Python 3.6.' but does not list any specific libraries or solvers with their version numbers beyond the programming language itself. |
| Experiment Setup | Yes | The parameter of sparsity constraints is set to s = 5. The parameters of constraints are set to st = 8 for all t [T] and s = 5T. The dimension is set to d = 64, which corresponds to images of size 8 x 8 pixels. The size of the ground set is n = 256. k = 20 and s = 5 for the online setting. KSVD [2], which is set to stop when the change of the objective value becomes no more than 10^-6 or 200 iterations are finished. |