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].
A Kernel Random Matrix-Based Approach for Sparse PCA
Authors: Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 discusses the practical aspects and provides experimental results. Section 6 concludes the article. |
| Researcher Affiliation | Collaboration | 1CEA List, 2Centrale Supรฉlec, 3GIPSA-Lab University of Grenoble Alpes EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | The PCs ui, for i [4] are the Three Peak , Piece Poly , Step New and Sing signals of (Johnstone & Lu, 2009). |
| Dataset Splits | Yes | The soft-parameters a and ฯ (respectively for our method and CT) are selected by cross-validation using a validation set of size n. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | Yes | We use p = 2048, n = 1024. The soft-parameters a and ฯ (respectively for our method and CT) are selected by cross-validation using a validation set of size n. The selected parameters are a = 20 and ฯ = 0.1. |