High-dimensional multi-view clustering methods
This is an incremental review paper for researchers in multi-view clustering, focusing on high-dimensional data analysis.
The paper examines and compares tensor-based multi-view clustering approaches, specifically graph-based and subspace-based methods, on benchmark datasets to address challenges in combining multiple data views.
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.