A Review of Deep Multi-View Clustering Methods
Keywords:
Data Mining, Deep Learning, Unsupervised Learning, Multi-View Clustering, Feature RepresentationAbstract
With the advent of the big data era, multi-view data has become an indispensable and crucial data format in numerous real-world applications. As an effective unsupervised learning approach, multi-view clustering (MVC) can fully exploit the information from multi-view data without requiring labels, thereby uncovering its inherent clustering structures. In recent years, advancements in deep learning technologies have significantly propelled the innovation of multi-view clustering methods. Deep multi-view clustering (DMVC) methods have garnered substantial attention due to their exceptional advantages in non-linear feature learning, unsupervised clustering, and multi-source data fusion. This paper provides a comprehensive review of the latest research advancements in DMVC methods, with a focus on different strategies such as autoencoder-based, self-representation, contrastive learning, and ensemble learning approaches. An in-depth analysis of the characteristics, strengths, and limitations of each method is also presented. Furthermore, the current challenges in the DMVC domain are summarized, and insights into future research directions are provided, aiming to offer valuable references for relevant researchers.
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