Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC.

翻译：多视图聚类（MVC）是一种利用多种数据源提升聚类性能的流行技术。然而，现有方法主要关注获取一致信息，往往忽视了多视图间冗余问题。本研究提出一种名为充分多视图聚类（SUMVC）的新方法，从信息论角度审视多视图聚类框架。我们提出的方法包含两部分：首先，开发一种简单可靠的多视图聚类方法SCMVC（简单一致多视图聚类），其采用变分分析生成一致信息；其次，提出充分表示下界以增强一致信息并最小化视图间的非必要信息。所提出的SUMVC方法为多视图聚类问题提供了有前景的解决方案，并为分析多视图数据提供了新视角。为验证模型有效性，我们基于贝叶斯错误率进行了理论分析，并在多个多视图数据集上的实验证明SUMVC具有优越性能。