Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment

TL;DR

This paper introduces a novel dual optimization framework using contrastive learning, meta-learning, and deep models to improve partial multi-view clustering, especially with incomplete data, outperforming existing methods.

Contribution

It presents a new contrastive learning-based dual optimization framework that effectively handles incomplete multi-view data through meta-learning and deep feature alignment.

Findings

Outperforms state-of-the-art clustering models on benchmark datasets.

Effectively handles complex and incomplete multi-view data.

Utilizes a combination of Vision Transformer and KNN for missing view imputation.

Abstract

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.