Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment
This addresses a practical challenge in data analysis for real-world applications with incomplete multi-view data, representing an incremental improvement over existing methods.
The paper tackles the problem of partial multi-view clustering where some data views are missing, proposing a dual optimization framework with contrastive learning and meta-learning to improve clustering performance, achieving state-of-the-art results on BDGP and HW datasets.
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.