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Robust latent poisson deconvolution from multiple features for web topic detection
Junbiao Pang; Fei Tao; Chunjie Zhang; Weigang Zhang; Qingming Huang; Baocai Yin
2016
发表期刊IEEE TRANSACTIONS ON MULTIMEDIA
卷号18期号:12页码:2482-2493
摘要Detecting “hot” topics from the enormous usergenerated
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-constrainedUGC.
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 showsignificantly 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 Multi-view Learning User Generated Content Web Topic Detection
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15390
专题类脑智能研究中心
推荐引用方式
GB/T 7714
Junbiao Pang,Fei Tao,Chunjie Zhang,et al. Robust latent poisson deconvolution from multiple features for web topic detection[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2016,18(12):2482-2493.
APA Junbiao Pang,Fei Tao,Chunjie Zhang,Weigang Zhang,Qingming Huang,&Baocai Yin.(2016).Robust latent poisson deconvolution from multiple features for web topic detection.IEEE TRANSACTIONS ON MULTIMEDIA,18(12),2482-2493.
MLA Junbiao Pang,et al."Robust latent poisson deconvolution from multiple features for web topic detection".IEEE TRANSACTIONS ON MULTIMEDIA 18.12(2016):2482-2493.
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