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..
A Local Non-Negative Pursuit Method for Intrinsic Manifold Structure Preservation
Authors: Dongdong Chen, Jian Cheng Lv, Zhang Yi
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis and experimental results show that the proposed method achieves or outperforms the state-of-the-art results on various manifold learning problems. |
| Researcher Affiliation | Academia | Dongdong Chen and Jian Cheng Lv and Zhang Yi Machine Intelligence Laboratory College of Computer Science, Sichuan University Chengdu 610065, P. R. China |
| Pseudocode | Yes | Algorithm 1 Find Aopt: Local Non-negative Pursuit |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper mentions datasets like 'COIL-20' and 'Extended Yale B', which are well-known, but it does not provide a direct URL, DOI, repository name, or a formal citation with author and year for accessing these datasets. |
| Dataset Splits | No | The paper discusses data usage for manifold embedding and clustering but does not specify training, validation, or test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | All the experiments were carried out using MATLAB on a 2.2 GHz machine with 2.0GB RAM. |
| Software Dependencies | No | The paper mentions 'MATLAB' but does not provide a specific version number for the software used. |
| Experiment Setup | Yes | The paper specifies experimental parameters such as neighborhood size 'K = 2, 3, ..., 100' and 'λ = 60/100 for SMCE', which are settings used in the experiments. |