20 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…
AGPO: Asymmetric Group Policy Optimization for Verifiable Reasoning and Search Ads Relevance at JD
Yang Xu, Kun Yao, Yiming Deng +3
Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated notable success in enhancing the reasoning performance of large language models (LLMs). However, recent studi…
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…
Mass Distribution versus Density Distribution in the Context of Clustering
Kai Ming Ting, Ye Zhu, Hang Zhang +1
This paper investigates two fundamental descriptors of data, i.e., density distribution versus mass distribution, in the context of clustering. Density distribution has been the de…
A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data
Di Su, Kai Ming Ting, Jie Zhang +2
The detection of rare cell types in single-cell transcriptomics data is crucial for elucidating disease pathogenesis and tissue development dynamics. However, a critical gap that p…
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval
Zhibo Zhang, Yang Xu, Kai Ming Ting +1
Large language models (LLMs) have recently enabled remarkable progress in text representation. However, their embeddings are typically high-dimensional, leading to substantial stor…
SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection
Yang Xu, Hang Zhang, Yixiao Ma +2
The core problem in multi-view anomaly detection is to represent local neighborhoods of normal instances consistently across all views. Recent approaches consider a representation…
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…
GeoPTH: A Lightweight Approach to Category-Based Trajectory Retrieval via Geometric Prototype Trajectory Hashing
Yang Xu, Zuliang Yang, Kai Ming Ting
Trajectory similarity retrieval is an important part of spatiotemporal data mining, however, existing methods have the following limitations: traditional metrics are computationall…
Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier
Xinpeng Li, Kai Ming Ting
The proliferation of complex, black-box AI models has intensified the need for techniques that can explain their decisions. Feature attribution methods have become a popular soluti…
Contrastive Multi-View Graph Hashing
Yang Xu, Zuliang Yang, Kai Ming Ting
Multi-view graph data, which both captures node attributes and rich relational information from diverse sources, is becoming increasingly prevalent in various domains. The effectiv…
Voronoi Diagram Encoded Hashing
Yang Xu, Kai Ming Ting
The goal of learning to hash (L2H) is to derive data-dependent hash functions from a given data distribution in order to map data from the input space to a binary coding space. Des…
An Online Automatic Modulation Classification Scheme Based on Isolation Distributional Kernel
Xinpeng Li, Zile Jiang, Kai Ming Ting +1
Automatic Modulation Classification (AMC), as a crucial technique in modern non-cooperative communication networks, plays a key role in various civil and military applications. How…
RAD: A Realistic Multi-View Benchmark for Pose-Agnostic Anomaly Detection
Kaichen Zhou, Xinhai Chang, Taewhan Kim +8
Anomaly detection is essential for robotic perception and industrial inspection, yet most benchmarks are collected under controlled conditions with fixed viewpoints and stable illu…
Distributed Clustering based on Distributional Kernel
Hang Zhang, Yang Xu, Lei Gong +2
This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC that produces the final cluste…