Higher Order Multiple Graph Filtering for Structured Graph Learning
Liang Du, Xiaodong Li, Yan Chen, Gui Yang, Mian Ilyas Ahmad, Peng Zhou
Abstract
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.
BibTeX
@inproceedings{icassp2024_higherordermulti,
title = {Higher Order Multiple Graph Filtering for Structured Graph Learning},
author = {Liang Du and Xiaodong Li and Yan Chen and Gui Yang and Mian Ilyas Ahmad and Peng Zhou},
booktitle = {ICASSP 2024},
year = {2024}
}