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..
Spherical Rotation Dimension Reduction with Geometric Loss Functions
Authors: Hengrui Luo, Jeremy E. Purvis, Didong Li
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive simulation study, along with a successful application to human cell cycle data, further highlights the advantages of SRCA compared to state-of-the-art alternatives, demonstrating its superior performance in approximating the manifold while preserving inherent geometric structures. [...] Section 4. Numerical Experiments |
| Researcher Affiliation | Academia | Hengrui Luo EMAIL Lawrence Berkeley National Laboratory Berkeley, CA, 94720, USA Department of Statistics, Rice University Houston, TX, 77005, USA; Jeremy E. Purvis EMAIL Department of Genetics University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA; Didong Li EMAIL Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA |
| Pseudocode | Yes | Algorithm 1: SRCA dimension reduction algorithm; Algorithm 2: SRCA dimension reduction algorithm with l1 relaxation; Algorithm 3: SRCA dimension reduction algorithm with branch-and-bound |
| Open Source Code | Yes | Our code for SRCA implementations and experiments are publicly available at https://github.com/hrluo/Spherical Rotation Dimension Reduction. |
| Open Datasets | Yes | UCI repository (https://archive.ics.uci.edu/ml): Banknote, Climate, Concrete, Ecoli1, Leaf, Power Plant, User Knowledge. Microarray: Alon (Alon et al., 1999). GTEx (https://gtexportal.org/home/). |
| Dataset Splits | No | Although the paper mentions using "out-sample MSEs" and discusses various datasets, it does not provide specific details on the training, validation, or test split percentages or methodology (e.g., "80/10/10 split", "5-fold cross-validation"). |
| Hardware Specification | No | The paper discusses computational time and complexity, but does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions using the "co Ranking R-package" but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | The first parameter that dictates the behavior of most DR methods is the retained dimension d , which can be determined by subsequent purpose (e.g., the t SNE and UMAP usually take d = 2, 3 for visualizations). The second parameter is the choice of rotation methods, which is highly data dependent and affects the clustering and visualization most. [...] To account for the diverse units of the 40 selected features, we applied z-score normalization to the data. [...] In the situation where the tail behavior of the noise is close to Gaussian and the W is known (or, by default I), PCA is our default choice; but in the situation where the noise is non-Gaussian and we do not have much knowledge for W, then ICA (Hyv arinen and Oja, 2000) is a better alternative. [...] Here, we provide a new version of SRCA with sparse penalty, which only involves an additional penalty term in the loss function we designed. [...] with a tuning parameter ΞΎ > 0. |