On Order-Constrained Transitive Distance Clustering
Authors: Zhiding Yu, Weiyang Liu, Wenbo Liu, Yingzhen Yang, Ming Li, B. V. K. Vijaya Kumar
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on toy, image and speech datasets show the excellent performance of OCTD, surpassing TD with significant gains and giving state-of-the-art performance on several datasets. |
| Researcher Affiliation | Academia | Dept. of Electrical and Computer Engineering, Carnegie Mellon University School of Electronic and Computer Engineering, Peking University, P.R. China SYSU-CMU Joint Institute of Engineering, Sun Yat-sen University Dept. of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | The Extended Yale B dataset (Ex YB) contains 2414 frontal-faces (192 168) of 38 subjects. ... For the AR face dataset (Martınez and Benavente 1998)... The USPS dataset contains 9298 16 16 handwritten digit images. ... The NIST and Switch Board datasets are formed by extracting the i-vectors under the framework of (Li and Narayanan 2014)3. |
| Dataset Splits | No | The paper references datasets but does not explicitly provide specific training/validation/test dataset split percentages, sample counts, or detailed splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact 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 (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For OCTD (Min) and OCTD (Mean), having a sample rate of 0.3 and 500 diversified TD matrices works well on most examples. ... The sample rates on the two examples are increased to 0.8. In addition, the KNN number for bandwidth estimation on Pathbased is reduced to 2. ... We fix the sample rate of OCTD (Min) to 0.06 and OCTD (Mean) to 0.2, while the random sampling numbers of both methods are set to 2000. |