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.
['Junbiao Pang', 'Fei Tao', 'Chunjie Zhang', 'Weigang Zhang', 'Qingming Huang', 'Baocai Yin']
Robust Latent Poisson Deconvolution From Multiple Features for Web Topic Detection