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Robust Latent Poisson Deconvolution From Multiple Features for Web Topic Detection

文献类型：期刊论文

作者Pang, Junbiao1; Tao, Fei2; Zhang, Chunjie2; Zhang, Weigang3,4; Huang, Qingming4,5; Yin, Baocai6,7
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2016-12-01
卷号18期号:12页码:2482-2493
关键词K-nearest neighbor similarity graph latent poisson deconvolution (LPD) multi-view learning (MVL) user-generated content (UGC) web topic detection
ISSN号1520-9210
DOI10.1109/TMM.2016.2598439
英文摘要Detecting "hot" topics from the enormous user-generated content (UGC) data on web poses two main difficulties that the conventional approaches can barely handle: 1) poor feature representations from noisy images or short texts, and 2) uncertain roles of modalities where the visual content is either highly or weakly relevant to the textual cues due to the less-constrained UGC. In this paper, following the detection-by-ranking approach, we address above challenges by learning a robust latent representation from multiple, noisy and a high probability of the complementary features. Both the textual features and the visual ones are encoded into a k-nearest neighbor hybrid similarity graph (HSG), where nonnegative matrix factorization using random walk is introduced to generate topic candidates. An efficient fusion of multiple HSGs is then done by a latent poisson deconvolution, which consists of a poisson deconvolution with sparse basis similarity for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public datasets.
资助项目National Basic Research Program of China (973 Program)[2012CB316400] ; National Basic Research Program of China (973 Program)[2015CB3351800] ; Natural Science Foundation of China[61332016] ; Natural Science Foundation of China[61472387] ; Natural Science Foundation of China[61303153] ; Natural Science Foundation of China[61390510] ; Natural Science Foundation of China[61303154] ; Beijing Post-Doctoral Research Foundation ; Beijing Municipal Commission of Education[KM201610005034] ; Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (PHR)`
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000388920200014
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/7830]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Weigang
作者单位1.Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China
4.Univ Chinese Acad Sci, Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
6.Dalian Univ Technol, Adv Invocat Ctr Future Internet Technol, Dalian 116024, Peoples R China
7.Beijing Univ Technol, Beijing 100124, Peoples R China
推荐引用方式
GB/T 7714		 Pang, Junbiao,Tao, Fei,Zhang, Chunjie,et al. Robust Latent Poisson Deconvolution From Multiple Features for Web Topic Detection[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2016,18(12):2482-2493.
APA Pang, Junbiao,Tao, Fei,Zhang, Chunjie,Zhang, Weigang,Huang, Qingming,&Yin, Baocai.(2016).Robust Latent Poisson Deconvolution From Multiple Features for Web Topic Detection.IEEE TRANSACTIONS ON MULTIMEDIA,18(12),2482-2493.
MLA Pang, Junbiao,et al."Robust Latent Poisson Deconvolution From Multiple Features for Web Topic Detection".IEEE TRANSACTIONS ON MULTIMEDIA 18.12(2016):2482-2493.

入库方式： OAI收割

来源：计算技术研究所

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