Advances in Multi-View Clustering: Theory, Algorithms, and Applications

Dear Colleagues,

Multi-view clustering aims to group data samples from a multiple features perspective, each corresponding to a distinct property of the data objects. How to effectively leverage each view- specific feature representation while achieving consensus among all of them is a crucial issue; the basic goal of multi-view clustering is to achieve the consensus clustering structure across all views. Sometimes, different views contribute with varied weights to the final clustering output, hence learning their weights is also critical to yield the ideal clustering result. Worse still, the problem of missing views often occurs; therefore, we aim to address this issue in the context of incomplete multi-view clustering. These fundamental problems give rise to another challenge–how to leverage multi-views efficiently, especially for multi-view graph data in order to obtain structured clustering outputs. More precisely, we are currently focusing on deep neural networks to map each view to specific non-linear deep latent space to achieve the ideal alignment across all views.

In addition, unsupervised learning plays a pivotal role in data analytics, extending even to multi-modal learning such as text-to-image (T2I) generation, transformers, robust adversarial learning, and other approaches, which further provide the opportunity to be closely integrated with advanced deep models. Altogether, these advancements enable us to achieve precise, scalable, and diverse unsupervised grouping patterns for multi-view clustering models.

I look forward to receiving your contributions.

Prof. Dr. Yang Wang
Guest Editor

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