2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDMLSP-8.2
Paper Title RGLN: Robust Residual Graph Learning Networks via Similarity-Preserving Mapping on Graphs
Authors Jiaxiang Tang, Xiang Gao, Wei Hu, Peking University, China
SessionMLSP-8: Learning
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-SSUP] Self-supervised and semi-supervised learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Graph Convolutional Neural Networks (GCNNs) extend CNNs to irregular graph data domain, such as brain networks, citation networks and 3D point clouds. It is critical to identify an appropriate graph for basic operations in GCNNs. Existing methods often manually construct or learn one fixed graph based on known connectivities, which may be sub-optimal. To this end, we propose a residual graph learning paradigm to infer edge connectivities and weights in graphs, which is cast as distance metric learning under a low-rank assumption and a similarity-preserving regularization. In particular, we learn the underlying graph based on similarity-preserving mapping on graphs, which keeps similar nodes close and pushes dissimilar nodes away. Extensive experiments on semi-supervised learning of citation networks and 3D point clouds show that we achieve the state-of-the-art performance in terms of both accuracy and robustness.