科研成果详情

题名An online approach for learning non-Gaussian mixture models with localized feature selection
作者
发表日期2013
会议名称1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA)
会议录名称2013 1st International Conference on Communications, Signal Processing and Their Applications, ICCSPA 2013
会议日期FEB 12-14, 2013
会议地点Amer Univ Sharjah, Sharjah, U ARAB EMIRATES
摘要

This paper presents an online algorithm for mixture model-based clustering. Online algorithms allow data points to be processed sequentially, which is critical for real-time and large-scale applications. The proposed online algorithm is based on finite generalized Dirichlet (GD) mixtures together with a unsupervised localized feature selection scheme. By learning the proposed model in an online manner through a variational inference framework, all the involved model parameters, number of components and features weights are estimated simultaneously in closed forms. Additionally, the problem of overfitting is avoided due to the nature of Bayesian learning. The proposed method is validated by both synthetic data and an application concerning the online automatic image annotation. © 2013 IEEE.

关键词generalized Dirichlet mixtures localized feature selection mixture model online learning variational Bayes
DOI10.1109/ICCSPA.2013.6487242
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收录类别CPCI-S
语种英语English
WOS研究方向Engineering ; Telecommunications
WOS类目Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000318427600017
Scopus入藏号2-s2.0-84876037720
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/13299
专题个人在本单位外知识产出
理工科技学院
通讯作者Fan, Wentao
作者单位
Concordia Institute for Information Systems Engineering,Concordia University,Montreal, QC,Canada
推荐引用方式
GB/T 7714
Fan, Wentao,Bouguila, Nizar. An online approach for learning non-Gaussian mixture models with localized feature selection[C], 2013.
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