Multi-view clustering has been widely used in recent years in comparison to single-view clustering, for clear reasons, as it offers more insights into the data, which has brought with it some challenges, such as how to combine these views or features. Most of recent work in this field focuses mainly on tensor representation instead of treating the data as simple matrices. This permits to deal with the high-order correlation between the data which the based matrix approach struggles to capture. Accordingly, we will examine and compare these approaches, particularly in two categories, namely graph-based clustering and subspace-based clustering. We will conduct and report experiments of the main clustering methods over a benchmark datasets.

翻译：多视角聚类近年来相较于单视角聚类得到广泛应用，其原因显而易见——它能提供更丰富的数据洞察，同时也带来了一些挑战，例如如何整合这些视角或特征。该领域近期的大多数工作主要聚焦于张量表示，而非将数据视为简单矩阵。这有助于处理基于矩阵的方法难以捕获的数据间高阶相关性。因此，我们将重点考察并比较两类方法：基于图的聚类和基于子空间的聚类。我们将在基准数据集上开展主要聚类方法的实验并报告结果。