Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning
Author:
Affiliation:
1. College of Computer and Data Science, Fuzhou University, Fuzhou, China
2. School of Cyberspace Security, Chengdu University of Information Technology, Chengdu, China
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6046/10016790/10080867.pdf?arnumber=10080867
Reference46 articles.
1. Jointly Learning Kernel Representation Tensor and Affinity Matrix for Multi-View Clustering
2. A Semisupervised Classification Approach for Multidomain Networks With Domain Selection
3. Learnable Multi-view Matrix Factorization with Graph Embedding and Flexible Loss
4. Efficient and Effective Regularized Incomplete Multi-view Clustering
5. DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Discriminative Multi-Scale Deep Features
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