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Wentao Fan
Researcher at Huaqiao University
Publications - 123
Citations - 1259
Wentao Fan is an academic researcher from Huaqiao University. The author has contributed to research in topics: Mixture model & Dirichlet distribution. The author has an hindex of 14, co-authored 101 publications receiving 895 citations. Previous affiliations of Wentao Fan include Concordia University Wisconsin & Concordia University.
Papers
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Journal ArticleDOI
Fast density peak clustering for large scale data based on kNN
Yewang Chen,Xiaoliang Hu,Wentao Fan,Lianlian Shen,Zheng Zhang,Xin Liu,Xin Liu,Ji-Xiang Du,Haibo Li,Yi Chen,Hailin Li +10 more
TL;DR: A simple but fast DPeak, namely FastDPeak, 1 is proposed, which runs in about O ( n l o g ( n ) ) expected time in the intrinsic dimensionality and replaces density with kNN-density, which is computed by fast kNN algorithm such as cover tree, yielding huge improvement for density computations.
Journal ArticleDOI
Variational Learning for Finite Dirichlet Mixture Models and Applications
TL;DR: This paper focuses on the variational learning of finite Dirichlet mixture models and suggests that this approach has several advantages: first, the problem of over-fitting is prevented; furthermore, the complexity of the mixture model can be determined automatically and simultaneously with the parameters estimation as part of the Bayesian inference procedure.
Journal ArticleDOI
Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection
Wentao Fan,Nizar Bouguila +1 more
TL;DR: The experimental results reported for both synthetic data and real-world challenging applications involving image categorization, automatic semantic annotation and retrieval show the ability of the approach to provide accurate models by distinguishing between relevant and irrelevant features without over- or under-fitting the data.
Proceedings ArticleDOI
Unsupervised Anomaly Intrusion Detection via Localized Bayesian Feature Selection
TL;DR: This paper proposes a novel unsupervised statistical approach for detecting network based attacks through finite generalized Dirichlet mixture models, in the context of Bayesian variational inference.
Journal ArticleDOI
Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference
Wentao Fan,Nizar Bouguila +1 more
TL;DR: A principled approach for approximating the intractable model's posterior distribution by a tractable one is proposed-which is developed-such that all the involved mixture's parameters can be estimated simultaneously and effectively in a closed form.