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].
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. |