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 [1].
Stationary Diffusion State Neural Estimation for Multiview Clustering
Authors: Chenghua Liu, Zhuolin Liao, Yixuan Ma, Kun Zhan7542-7549
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several multiview datasets demonstrate effectiveness of SDSNE in terms of six clustering evaluation metrics. |
| Researcher Affiliation | Academia | School of Information Science and Engineering, Lanzhou University EMAIL |
| Pseudocode | Yes | Algorithm 1: SDSNE for multiview clustering. |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | Yes | Six benchmark datasets are used to demonstrate the effectiveness of the proposed method, including BBC Sport1: ... MSRC-v12: ... 100 Leaves3: ... Three Sources4: ... Scene-15 (Fei-Fei and Perona 2005): ... Reuters5: ... |
| Dataset Splits | No | The paper mentions running experiments multiple times and using an early stop strategy, but it does not specify explicit training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions setting 'the seed of the pseudo-random generator as in GCN (Kipf and Welling 2017)' but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The learning rate is set to be 10 4 or 10 5. ... the σ in the Gaussian kernel function is set to 0.5. ... an early stop strategy with the patience of 10 and stop training when the loss function drops dramatically. |