Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Gaussian Regression-Driven Tensorized Incomplete Multi-View Clustering with Dual Manifold Regularization
Authors: Zhenhao Zhong, Zhibin Gu, Pengpeng Yang, Yaqian zhou, Ruiqiang Guo
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 29,818 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,394 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six datasets demonstrate that our method outperforms SOTA approaches. |
| Researcher Affiliation | Academia | 1College of Computer and Cyber Security, Hebei Normal University, China 2College of computer and Information Technology, China Three Gorges University, China |
| Pseudocode | Yes | Algorithm 1 Optimization Algorithm of GUITAR |
| Open Source Code | Yes | The code is available at https://github.com/Rockfire Tip/GUITAR. |
| Open Datasets | Yes | We conduct experiments on six datasets: Yale3 [40], MSRC_v1 [41], EYale B10 [42], COIL20MV, Mfeat [43], and Scene [44]. |
| Dataset Splits | No | The paper describes how incomplete multi-view data is constructed by randomly setting a fraction of samples to zero (missing rates r = {0.1, 0.3, 0.5}), which defines experimental conditions. However, it does not specify explicit training, validation, or test dataset splits in the conventional machine learning sense for model learning or evaluation. |
| Hardware Specification | Yes | All experiments are conducted in MATLAB R2023b on a machine equipped with a 2.30 GHz i7-12650H CPU and 16GB RAM. |
| Software Dependencies | Yes | All experiments are conducted in MATLAB R2023b on a machine equipped with a 2.30 GHz i7-12650H CPU and 16GB RAM. |
| Experiment Setup | Yes | For our method, we search for the optimal values of λ1, λ2, and λ3 from the set {10 3, 10 2, 10 1, 100, 101}, while δ is tuned over {10 3, 10 2, 10 1, 100}. ACC is used as the evaluation criterion. As illustrated in Figure 2, the performance on the Mfeat dataset remains consistently stable when λ1, λ2, and λ3 are chosen from {10 3, 10 2, 10 1}. Algorithm 1 ... 1: Initialize: v, Zv = 1, Ev = 0, Yv = 0, G = 0, Q = 0, µ = 10 4, ρ = 10 4, ηµ = ηρ = 1.2, µmax = ρmax = 1012, ϵ = 10 7 |