Data Clustering by Laplacian Regularized L1-Graph
Authors: Yingzhen Yang, Zhangyang Wang, Jianchao Yang, Jiangping Wang, Shiyu Chang, Thomas Huang
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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 {yyang58, zwang119, jwang63, chang87, huang}@ifp.uiuc.edu, jiayang@adobe.com |
| 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. |