7 papers
Topologically Relevant Landmark Selection on Point Clouds
Kaifeng Zhang, Kai Ming Ting
Persistent Homology (PH) is an important tool in Topological Data Analysis for point clouds, but can be prohibitively expensive for large datasets. A practical approximation is to…
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
Hang Zhang, Kaifeng Zhang, Yixiao Ma +3
Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data $D_…
Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams
Kaifeng Zhang, Kai Ming Ting
Persistence Diagram (PD) is known to capture point cloud topology effectively, but its computation has high time complexity. Expected Persistence Diagram (EPD) has been developed t…
Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory
Kaifeng Zhang, Kai Ming Ting, Sanjay Chawla
Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point similarity function to find simil…
Optimization-Free Graph Embedding via Distributional Kernel for Community Detection
Shuaibin Song, Kai Ming Ting, Kaifeng Zhang +1
Neighborhood Aggregation Strategy (NAS) is a widely used approach in graph embedding, underpinning both Graph Neural Networks (GNNs) and Weisfeiler-Lehman (WL) methods. However, NA…
Rethinking Divisive Hierarchical Clustering from a Distributional Perspective
Kaifeng Zhang, Kai Ming Ting, Tianrun Liang +1
We uncover that current objective-based Divisive Hierarchical Clustering (DHC) methods produce a dendrogram that does not have three desired properties i.e., no unwarranted splitti…
IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection
Yang Xu, Yixiao Ma, Kaifeng Zhang +2
Anomaly detection on data streams presents significant challenges, requiring methods to maintain high detection accuracy among evolving distributions while ensuring real-time effic…