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:近年来,与单一观点分组相比,多观点分组得到广泛使用,原因很明显,因为它对数据有更深入的了解,因而带来了一些挑战,例如如何将这些观点或特征结合起来。该领域最近的工作大多主要侧重于“有代表性”而不是将数据作为简单矩阵处理。这样就可以处理基础矩阵方法难以捕捉的数据之间的高度相关性。因此,我们将审查和比较这些方法,特别是两类方法,即基于图形的分组和基于空间的子分组。我们将对基准数据集进行主要分组方法的实验并提出报告。</s>