41 citations · 47 across the 10 of their papers we have counts for
5 papers · 1 filter
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Zheng Wang, Hongming Ding, Li Pan +3
Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph…
Expanding Semantic Knowledge for Zero-shot Graph Embedding
Zheng Wang, Ruihang Shao, Changping Wang +3
Zero-shot graph embedding is a major challenge for supervised graph learning. Although a recent method RECT has shown promising performance, its working mechanisms are not clear an…
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
Jielei Chu, Jing Liu, Hongjun Wang +3
The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of…
Multi-local Collaborative AutoEncoder
Jielei Chu, Hongjun Wang, Jing Liu +2
The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborat…
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
Jielei Chu, Hongjun Wang, Jing Liu +2
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering i…