Robust Latent Poisson Deconvolution From Multiple Features for Web Topic Detection

doi:10.1109/TMM.2016.2598439

	Robust Latent Poisson Deconvolution From Multiple Features for Web Topic Detection
	Pang, Junbiao 1; Tao, Fei 2; Zhang, Chunjie 2; Zhang, Weigang 3,4; Huang, Qingming 4,5; Yin, Baocai 6,7
	2016-12-01
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
卷号	18 期号:12 页码:2482-2493
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.
关键词	K-nearest neighbor similarity graph latent poisson deconvolution (LPD) multi-view learning (MVL) user-generated content (UGC) web topic detection
DOI	10.1109/TMM.2016.2598439
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000388920200014
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7831
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.