Self-Weighted Graph-Based Framework for Multi-View Clustering

Abstract

Multiple perspectives can be used to explore rich and complex datasets that are widely used in many applications. However, in real-world applications, the multi-view data are often noisy because of various environmental factors. The key challenge of graph-based multi-view clustering is obtaining a consistent clustering structure. Most graph-based methods learn independently in one view how similar the data points in each view are to one another. However, the consistency of information in a multi-view dataset is easily overlooked, causing an unsatisfactory unified graph matrix. To address this problem, this study proposes a novel self-weighted graph-based framework for multi-view clustering (SGMF). This framework integrates these initial similar graph matrices into a unified graph matrix by assigning different weights to the initial graphs of each view. This algorithm's goal is to effectively learn a unified graph matrix and eliminate noise or irrelevant information from the dataset. This is the first time a generalized multi-view framework based on the L1-norm has been proposed. L1-norm can be used to improve the robustness of the algorithm. Particularly, SGMF can automatically weigh each view's initial similar graph matrix to solve the noise problem and obtain a consistent data structure. Simultaneously, a rank constraint is imposed on the graph Laplacian matrix of the final unified matrix, which helps to automatically divide the data points into the appropriate number of clusters. The experimental results on synthetic and real datasets verify the effectiveness and superiority of the proposed method over the latest baseline.