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..
Selective Sampling-based Scalable Sparse Subspace Clustering
Authors: Shin Matsushima, Maria Brbic
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate effectiveness of our approach. |
| Researcher Affiliation | Academia | Shin Matsushima University of Tokyo EMAIL Maria Brbi c Stanford University EMAIL |
| Pseudocode | Yes | Pseudocode of representation learning step is summarized in Algorithm 1. |
| Open Source Code | Yes | Our code is available at https://github. com/smatsus/S5C. |
| Open Datasets | Yes | We verify the effectiveness of S5C on six benchmark datasets including face image dataset Yale B [36, 37], motion segmentation Hopkins 155 [38], object recognition datasets COIL-100 [39] and CIFAR-10 [40], handwritten digits dataset MNIST [41], letter recognition dataset of different fonts Letter-rec [42], and handwritten character recognition dataset Devanagari [43]. |
| Dataset Splits | Yes | The summary of datasets and details of experimental setup are provided in Appendix E. For Yale B, we used the standard splits as in [24, 25]. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory size, or specific machine names) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions software like GLMNET [31] and coordinate descent methods [32], but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In all experiments, we use only one random subsample, i.e., |I| = 1. For SSSC, we used batch size of 200, number of iterations T=10, λ=0.01. |