From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering
Authors: Shengju Yu, Zhibin Dong, Siwei Wang, Suyuan Liu, Ke Liang, Xinwang Liu, Yue Liu, Yi Zhang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple publicly available datasets confirm the effectiveness of proposed DTP-SF-BVF method. Concretely, we explicitly exploit the geometric properties between anchors via self-expression learning skill, and utilize topology learning strategy to feed captured anchoranchor features into anchor-sample graph so as to explore the manifold structure hidden within samples more adequately. |
| Researcher Affiliation | Academia | 1School of Computer, National University of Defense Technololgy, Hunan, China. 2Intelligent Game and Decision Lab, Beijing, China. EMAIL. Correspondence to: Siwei Wang <EMAIL>, Xinwang Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 The proposed DTP-SF-BVF Input: Multi-view data {Xp}v p=1, hyper-parameters λ, β. Output: Discrete cluster indicator matrix C. Initialize: {Ap, Tp, Bp, Sp}v p=1, C, α. 1: repeat 2: Update Ap via Eq. (5) 3: Update Tp via Eq. (7) 4: Update Bp via Eq. (9) 5: Update Sp via Eq. (11) 6: Update C via Eq. (14) 7: Update αp via Eq. (16) 8: until convergent |
| Open Source Code | No | The paper does not contain any explicit statement about code release or a link to a repository. Phrases like "We release our code..." or specific URLs are missing. |
| Open Datasets | Yes | Datasets We evaluate the algorithm performance on the following 7 datasets: DERMATO, CALTE7, Cora, REU7200, Reuters, CIF10Tra4, Fas MNI4V. |
| Dataset Splits | No | The paper mentions running experiments multiple times and calculating mean and variance, but it does not specify exact percentages, sample counts, or reference to predefined training/validation/test splits for the datasets used. |
| Hardware Specification | No | The paper does not mention any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific names of software libraries or tools along with their version numbers, which are necessary for reproducibility. |
| Experiment Setup | Yes | Parameter Setup We search the hyper-parameters λ and β in [10 1, 100, 101, 102, 103] and [2 4, 2 2, 20, 22, 24] respectively. For all competitors, we download their source code and tune the parameters according to their provided guidelines. Three popular metrics are employed to measure the clustering results. For fairness, we run 20 times and calculate the mean and variance of clustering results. |