Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification
Authors: Jie Wen, Yadong Liu, Zhanyan Tang, Yuting He, Yulong Chen, Mu Li, Chengliang Liu
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple benchmarks validate our advantages and demonstrate strong compatibility with both missing and complete data. 4. Experiments 4.1. Experimental Settings 4.2. Experimental Results and Analysis |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518000 China 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA. |
| Pseudocode | Yes | Algorithm 1 Training process of COME |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | Datasets: In line with previous works (Tan et al., 2018; Liu et al., 2023), we conduct experiments on five multi-view multi-label datasets, i.e., Corel5k (Duygulu et al., 2002), Pascal07 (Everingham et al., 2010), ESPGame (Von Ahn & Dabbish, 2004), IAPRTC12 (Grubinger et al., 2006), and Mirflickr (Huiskes & Lew, 2008). |
| Dataset Splits | Yes | (3) Dataset Splitting: Subsequently, 70% of the resulting samples are randomly selected as the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Algorithm 1 Training process of COME ... Initialization: Initialize the parameters of the model A and set hyper-parameters (λ1, λ2, β, and training epochs E) ... when the value of β is 0.1 and 1 for Corel5k and Pascal07 datasets, respectively, information compression and effective information reconstruction reach a balanced state, and the model achieves the optimal performance. |