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..
Data Clustering by Laplacian Regularized L1-Graph
Authors: Yingzhen Yang, Zhangyang Wang, Jianchao Yang, Jiangping Wang, Shiyu Chang, Thomas Huang
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on real data sets demonstrate the superiority of our algorithm compared to ℓ1-Graph and other competing clustering methods. |
| Researcher Affiliation | Collaboration | 1 Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. 2 Adobe Research, San Jose, CA 95110, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 describes the learning procedure for LRℓ1-Graph. Algorithm 1 Learning Procedure for LRℓ1-Graph |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The clustering results on several real data sets, i.e. UCI Wine, UCI Breast Tissue (BT) and ORL face database, are shown in Table 1 where the clustering performance is measured by Accuracy (AC) and the Normalized Mutual Information (NMI). |
| Dataset Splits | No | The paper mentions using real datasets (UCI Wine, UCI Breast Tissue, ORL face database) but does not specify details about training, validation, or test splits. It directly presents 'Clustering Results'. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We use fixed empirical value λ = 0.1, γ = 30, M = 2 throughout the experiments, and tune λ between [0.1, 1] for ℓ1-Graph and SMCE. |