解决多视图数据部分缺失时的聚类难题，提升真实场景下聚类效果。
Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment
- 用对比学习与元学习动态补全缺失视图并调整权重。
- 在BDGP和HW数据集上显著优于现有方法，尤其处理复杂不完整数据。
- 适合有缺失多源数据的科研与工业聚类任务使用。
部分多视图聚类（PVC）是现实应用中极具挑战性的实际问题，尤其当数据的部分视图缺失时。现有聚类方法难以有效处理不完整视图，导致聚类性能下降。本文提出一种基于对比学习的双优化框架，旨在最大化不完整多视图数据中潜在特征的一致性，并通过深度学习模型提升聚类性能。结合微调的Vision Transformer与k近邻（KNN），该方法可填补缺失视图，并利用自监督学习与元学习动态调整视图权重。实验表明，该框架在BDGP和HW数据集上优于当前最优聚类模型，尤其在处理复杂且不完整的多视图数据时表现突出。
原文摘要 · Abstract (English)
Partial multi-view clustering (PVC) presents significant challenges practical research problem for data analysis in real-world applications, especially when some views of the data are partially missing. Existing clustering methods struggle to handle incomplete views effectively, leading to suboptimal clustering performance. In this paper, we propose a novel dual optimization framework based on contrastive learning, which aims to maximize the consistency of latent features in incomplete multi-view data and improve clustering performance through deep learning models. By combining a fine-tuned Vision Transformer and k-nearest neighbors (KNN), we fill in missing views and dynamically adjust view weights using self-supervised learning and meta-learning. Experimental results demonstrate that our framework outperforms state-of-the-art clustering models on the BDGP and HW datasets, particularly in handling complex and incomplete multi-view data.
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