Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Dictionary Learning with Capped l1-Norm
Authors: Wenhao Jiang, Feiping Nie, Heng Huang
IJCAI 2015 | Venue PDF | 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. |