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..
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Authors: Manolis Tsakiris, Rene Vidal
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figs. 1(a)-1(b) show the subspace preserving accuracy for both PZF-SSC and ZF-SSC, along with a fitted hyperbola (allowing for vertical and horizontal shift) for the phase transition region, the latter measured with a precision of 0.98. Figs. 1(c)-1(d) show the corresponding clustering accuracies produced by spectral clustering applied on the affinity graphs. Parameters are set as D = 100, ρ = 5, n = 3, λ = 10/ ζ. The complete data are unit norm, drawn uniformly at random from the subspaces, and each point is missing m = ωD entries also chosen uniformly at random. Results are averaged over 10 trials. |
| Researcher Affiliation | Academia | 1School of Information Science and Technology, Shanghai Tech University, Shanghai, China. 2Mathematical Institute for Data Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to its own source code for the methodology described. |
| Open Datasets | No | The complete data are unit norm, drawn uniformly at random from the subspaces, and each point is missing m = ωD entries also chosen uniformly at random. |
| Dataset Splits | No | The paper does not specify train/validation/test dataset splits. Data is generated synthetically for experiments, not from a pre-existing dataset with standard splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Parameters are set as D = 100, ρ = 5, n = 3, λ = 10/ ζ. The complete data are unit norm, drawn uniformly at random from the subspaces, and each point is missing m = ωD entries also chosen uniformly at random. Results are averaged over 10 trials. |