Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering
Authors: Tingting Wu, Songhe Feng, Jiazheng Yuan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is compared with recent advances in experiments with different missing ratios on seven well-known datasets, demonstrating its effectiveness and the advantages of the proposed interpolation method. |
| Researcher Affiliation | Academia | Tingting Wu1,2, Songhe Feng1,2*, Jiazheng Yuan3* 1Tangshan Research Institute, Beijing Jiaotong University, Beijing, China 2Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Beijing, China 3College of Science and Technology, Beijing Open University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: The proposed Method |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We perform experiments on seven widely used multi-view benchmark datasets shown in Table 1, including HW2sources, UCI Digits, HW, Caltech101-20, BDGP fea, CCV and Hdigit, respectively. |
| Dataset Splits | No | The paper describes using 'missing ratios' and evaluating clustering performance on datasets, but it does not specify explicit train/validation/test splits, nor a distinct validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set λ in a range of 10^-4, 10^1 and conduct experiments to study the sensitivity and effect of the parameter on the clustering performance on all datasets. |