Spherical Rotation Dimension Reduction with Geometric Loss Functions
Authors: Hengrui Luo, Jeremy E. Purvis, Didong Li
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | 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. |