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 [1].
Multi-Response Linear Discriminant Analysis in High Dimensions
Authors: Kai Deng, Xin Zhang, Aaron J. Molstad
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate the effectiveness of our approach through simulation studies and applications to benchmark datasets. ... Section 8 and Section 9, respectively. |
| Researcher Affiliation | Academia | Kai Deng EMAIL Xin Zhang EMAIL Department of Statistics Florida State University Tallahassee, Florida, USA Aaron J. Molstad EMAIL School of Statistics University of Minnesota Minneapolis, Minnesota, USA |
| Pseudocode | Yes | Algorithm 1 Blockwise coordinate descent algorithm for joint classification (16). ... Algorithm A.1 ADMM algorithm update for conditional classification (13). |
| Open Source Code | No | The paper does not provide explicit links to source code repositories or statements about the release of source code. |
| Open Datasets | Yes | Simulation studies and real data examples are presented in Section 8 and Section 9, respectively. ... The descriptions of the dataset, which were obtained from https://www.uco.es/kdis/mllresources/, are as follows. Yeast. ... Image. ... Virus GO. ... Gpositive GO. ... Gnegative GO. |
| Dataset Splits | Yes | For 100 independent replications, classification performance is evaluated on a testing data of size 1000. All tuning parameters are selected by 5-fold cross-validation. ... For each dataset, we randomly split the data 100 times and keep 20% of the samples as a testing set. ... We use 4/5 of the samples for training and the remaining 1/5 for testing. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The above competitors are implemented by R packages msda, penalized LDA and glmnet. (No version numbers provided for R or the packages) |
| Experiment Setup | Yes | For all the simulated models, we set number of predictors p = 1000, sample size n = 30K where K = QM m=1 cm, and set the covariance Σ to be AR(0.3), unless specified otherwise. ... All tuning parameters are selected by 5-fold cross-validation. |