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..
Globally and Locally Consistent Unsupervised Projection
Authors: Hua Wang, Feiping Nie, Heng Huang
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been performed on six benchmark data sets, where the promising results validate the proposed method. |
| Researcher Affiliation | Academia | Department of Electrical Engineering and Computer Science Colorado School of Mines, Golden, Colorado 80401, USA Department of Computer Science and Engineering University of Texas at Arlington, Arlington, Texas 76019, USA |
| Pseudocode | Yes | Algorithm 1: The algorithm to solve Eq. (9). ... Algorithm 2: An ef๏ฌcient iterative algorithm to solve the general trace ratio minimization problem in Eq. (8). |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Six benchmark data sets are used in our experiments, including: two UCI data sets: the Dermatology and Ecoli data sets; one object data set: the COIL-20 data set; one digit and character data sets: the Binalpha data set; and two face data sets: the UMIST and AR data sets. |
| Dataset Splits | No | The paper mentions repeating experiments multiple times and reporting the best clustering performance after searching reduced dimensions, but it does not provide specific train/validation/test dataset splits, exact percentages, sample counts, or citations to predefined validation splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For KLE method and LPP method, we construct nearest-neighbor graph for each data set and set the neighborhood size for graph construction as 10 following (He and Niyogi 2004). ... in practice we empirically set K = 30 for simplicity... We repeat the experiments for 100 times to alleviate the impact of the random initialization of both our iterative algorithm and the K-means clustering method. |