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..
Unlabeled Principal Component Analysis
Authors: Yunzhen Yao, Liangzu Peng, Manolis Tsakiris
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess our algorithmic pipeline on synthetic data, face images, educational and medical records, with encouraging results. |
| Researcher Affiliation | Academia | Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris School of Information Science and Technology Shanghai Tech University yaoyzh,penglz,EMAIL |
| Pseudocode | Yes | Algorithm 1 Two-stage Algorithmic Pipeline for UPCA |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code for the methodology described. |
| Open Datasets | Yes | We use the well-known database Extended Yale B [14]... The ο¬rst dataset consists of the test scores of m = 707 high-school students... The second dataset consists of all the benign cases in Breast Cancer Wisconsin (Diagnostic) [12]. |
| Dataset Splits | No | The paper describes data generation parameters and outlier ratios, but does not specify train/validation/test dataset splits in the context of model training for reproduction. |
| Hardware Specification | Yes | Experiments are run on an Intel(R) i7-8700K, 3.7 GHz, 16GB machine. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We ο¬x m = 50, n = 500. With dim S = r = 1 : 1 : 49, we sample S at random from the Grassmannian Gr(r, m). Then n points x j are sampled at random from the intersection of S with the unit sphere of Rm to yield X . Let nin be the number of inliers Xin and nout the number of outliers Xout, with nin + nout = n. We consider outlier ratios nout/n = 0.1 : 0.1 : 0.9. |