11 citations · 20 across the 13 of their papers we have counts for
4 papers · 1 filter
Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods
Zhaiming Shen, Sung Ha Kang
Local clustering aims to identify specific substructures within a large graph without any additional structural information of the graph. These substructures are typically small co…
Tree-based Ensemble Learning for Out-of-distribution Detection
Zhaiming Shen, Menglun Wang, Guang Cheng +5
Being able to successfully determine whether the testing samples has similar distribution as the training samples is a fundamental question to address before we can safely deploy m…
Graph-based Semi-supervised Local Clustering with Few Labeled Nodes
Zhaiming Shen, Ming-Jun Lai, Sheng Li
Local clustering aims at extracting a local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size co…
A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction
Ming-Jun Lai, Zhaiming Shen
A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The alg…