Robust Dictionary Learning with Capped l1-Norm
Authors: Wenhao Jiang, Feiping Nie, Heng Huang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provided theoretical analysis and carried out extensive experiments on real word datasets and synthetic datasets to show the effectiveness of our method. |
| Researcher Affiliation | Academia | University of Texas at Arlington |
| Pseudocode | Yes | Algorithm 1 Robust dictionary learning with capped ℓ1-norm Algorithm 2 Weighted dictionary learning Algorithm 3 Dictionary update |
| Open Source Code | No | The paper does not provide any explicit statements or links for open-source code. |
| Open Datasets | Yes | extended Yale B dataset [Georghiades et al., 2001] and AR face dataset [Martinez, 1998]. |
| Dataset Splits | Yes | We split the database randomly into two halves. One half which contains about 32 images for each person was used for training the dictionary. The other half was used for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software components with version numbers. |
| Experiment Setup | Yes | In this experiment, we set the fraction of outliers as 0.05 and λ = 0.1 empirically. The dictionary size is 570 for all methods, which means 15 items for person on average. |