High-dimensional multi-view clustering methods

TL;DR

This paper reviews high-dimensional multi-view clustering methods, comparing tensor-based and matrix-based approaches, focusing on graph and subspace clustering, supported by experimental evaluations on benchmark datasets.

Contribution

It provides a comparative analysis of tensor and matrix approaches in multi-view clustering, highlighting their strengths and challenges in high-dimensional data.

Findings

Tensor-based methods better capture high-order correlations.

Graph-based clustering shows superior performance in certain datasets.

Experimental results demonstrate the effectiveness of different approaches.

Abstract

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.