Inclusivity induced adaptive graph learning for multi-view clustering

Abstract

Graph-based multi-view clustering, with its ability to mine potential associations between data samples, has attracted extensive attention. However, existing methods directly learn affinity graphs from multiple feature views without removing noisy information mixed with the original data, resulting in limited performance. In addition, only shared information is included in the graph fusion process, which excludes inconsistent information, resulting in insufficient exploration of complementary information among multi-view data. In this study, we propose an inclusivity-induced adaptive graph learning (IiAGL) method to address these issues. First, inclusivity is defined as the capability of a view to accommodate other views. Specifically, we extended subspace learning to graph construction, enabling view-specific graphs to capture the underlying data distribution. Furthermore, an inclusivity-induced regularizer is introduced to guide graph rebuilding and graph fusion, and the entire process is performed using a self-weighted strategy. In this manner, we can obtain a clean consensus containing shared representations and complementary information from different views. An alternating direction method with augmented Lagrangian multiplier (ADM-ALM) was designed to solve the resulting optimization problem. Extensive experiments on diverse datasets demonstrated that the clustering performance of the proposed method outperformed that of other state-of-the-art methods. The source code for this study is publicly available at https://github.com/ryan-xinzou/IiAGL-MC.