23 citations · 38 across the 12 of their papers we have counts for
13 papers
Testing Central Structure for Complex Networks
Qilong Wu, Yuxiao Li, Simeng Zhang +3
Centrality measures rank nodes even when the observed differences can be explained by ordinary structural variation. We therefore study a preceding question: whether a network cont…
Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancy
Mudi Jiang, Jiahui Zhou, Xinying Liu +2
Multi-view clustering (MVC) aims to uncover latent cluster structures by exploiting complementary information from multiple views. Despite substantial progress in clustering perfor…
Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering
Mudi Jiang, Jiahui Zhou, Xinying Liu +2
In multi-view clustering, the quality of different views may vary substantially, and low-quality or degraded views can impair overall clustering performance. However, existing stud…
Biological Sequence Clustering: A Survey
Simeng Zhang, Xinying Liu, Jun Lou +3
The rapid development of high-throughput sequencing technologies has led to an explosive increase in biological sequence data, making sequence clustering a fundamental task in larg…
Interpretable Fair Clustering
Mudi Jiang, Jiahui Zhou, Xinying Liu +2
Fair clustering has gained increasing attention in recent years, especially in applications involving socially sensitive attributes. However, existing fair clustering methods often…
Adversarial Fair Multi-View Clustering
Mudi Jiang, Jiahui Zhou, Lianyu Hu +3
Cluster analysis is a fundamental problem in data mining and machine learning. In recent years, multi-view clustering has attracted increasing attention due to its ability to integ…
Two-cluster test
Xinying Liu, Lianyu Hu, Mudi Jiang +3
Cluster analysis is a fundamental research issue in statistics and machine learning. In many modern clustering methods, we need to determine whether two subsets of samples come fro…
Interpretable Clustering Ensemble
Hang Lv, Lianyu Hu, Mudi Jiang +2
Clustering ensemble has emerged as an important research topic in the field of machine learning. Although numerous methods have been proposed to improve clustering quality, most ex…
Conjunction Subspaces Test for Conformal and Selective Classification
Zengyou He, Zerun Li, Junjie Dong +3
In this paper, we present a new classifier, which integrates significance testing results over different random subspaces to yield consensus p-values for quantifying the uncertaint…
Interpretable Clustering: A Survey
Lianyu Hu, Mudi Jiang, Junjie Dong +2
In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability. Howe…
Interpretable Multi-View Clustering
Mudi Jiang, Lianyu Hu, Zengyou He +1
Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world ap…
Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classification
Junjie Dong, Mudi Jiang, Lianyu Hu +1
Sequence classification has numerous applications in various fields. Despite extensive studies in the last decades, many challenges still exist, particularly in pattern-based metho…
Interpretable Sequence Clustering
Junjie Dong, Xinyi Yang, Mudi Jiang +2
Categorical sequence clustering plays a crucial role in various fields, but the lack of interpretability in cluster assignments poses significant challenges. Sequences inherently l…