Pseudo-label Enhanced Multi-view Deep Concept Factorization Fuzzy Clustering

Given the widespread availability of multi-view data in the real world, multi-view Fuzzy C-Means clustering (FCM) has garnered significant attention in recent years, leading to the development of various multi-view fuzzy clustering algorithms. However, existing algorithms still exhibit room for improvement. Firstly, most existing algorithms only utilize the shallow information of the view data and fail to delve into the mining and utilization of deeper representations. Secondly, existing algorithms tend to extract common representations among the views first and then implement clustering separately, which may lack a collaborative linkage between representation learning and the clustering task. Lastly, multi-view clustering algorithms based on representation learning often overlook the importance of effectively preserving similarity information within the views. To address these limitations, we propose a novel algorithm called Pseudo-Label Enhanced Multi-View Deep Concept Factorization Fuzzy Clustering (PE-MV-DCFCM). The algorithm first introduces a deep concept factorization method to uncover the deep information of the view data. Subsequently, it employs pseudo-label learning to preserve intra-view similarity information during the learning of common representations among the views, based on non-negative matrix factorization (NMF). Finally, this algorithm integrates deep concept factorization, representation learning, and fuzzy clustering into a unified framework to enhance the collaboration among the various sub-steps of the algorithm. Experiments on several benchmark datasets show that the proposed PE-MV-DCFCM algorithm outperformed other state-of-the-art algorithms.