566 citations · 685 across the 13 of their papers we have counts for
14 papers
Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
Qiuran Zhao, Kai Ming Ting, Xinpeng Li
The task of graph-level anomaly detection (GLAD) is to identify anomalous graphs that deviate significantly from the majority of graphs in a dataset. While deep GLAD methods have s…
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection
Zhong Zhuang, Kai Ming Ting, Guansong Pang +1
Graph anomaly detection has attracted a lot of interest recently. Despite their successes, existing detectors have at least two of the three weaknesses: (a) high computational cost…
A principled distributional approach to trajectory similarity measurement
Yufan Wang, Kai Ming Ting, Yuanyi Shang
Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i.e., they are computationally expensive and they can not guarantee the `uniq…
Detecting Change Intervals with Isolation Distributional Kernel
Yang Cao, Ye Zhu, Kai Ming Ting +4
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have…
Breaking the curse of dimensionality with Isolation Kernel
Kai Ming Ting, Takashi Washio, Ye Zhu +1
The curse of dimensionality has been studied in different aspects. However, breaking the curse has been elusive. We show for the first time that it is possible to break the curse u…
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection
Kai Ming Ting, Bi-Cun Xu, Takashi Washio +1
We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which convert a p…