12 papers · 1 filter
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…
How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning
Kai Ming Ting, Wei-Jie Xu, Hang Zhang
Deep clustering (DC) is often quoted to have a key advantage over -means clustering. Yet, this advantage is often demonstrated using image datasets only, and it is unclear wheth…
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…