Higher Order Multiple Graph Filtering for Structured Graph Learning

In the field of machine learning, multi-view clustering aims to reveal hidden clustering patterns across different data perspectives. However, traditional methods often struggle due to their reliance on low-order similarity data. To overcome this, we propose a new approach that integrates the learning of multiple graph filters, approximated through Chebyshev polynomials, with consensus structural graph learning into a unified framework. This method fully utilizes high-order statistical information from multiple data sources, thereby enhancing multi-view clustering. Comprehensive experiments conducted on multiple datasets consistently demonstrate significant performance improvements over traditional methods. Code is available at https://github.com/lxd1204/HMGC.