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..
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. |