UM
A probabilistic hierarchical model for multi-view and multi-feature classification
Li J.2; Yong H.2; Zhang B.1; Li M.2; Zhang L.2; Zhang D.2
2018
Source Publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Pages3498-3505
AbstractSome recent works in classification show that the data obtained from various views with different sensors for an object contributes to achieving a remarkable performance. Actually, in many real-world applications, each view often contains multiple features, which means that this type of data has a hierarchical structure, while most of existing works do not take these features with multi-layer structure into consideration simultaneously. In this paper, a probabilistic hierarchical model is proposed to address this issue and applied for classification. In our model, a latent variable is first learned to fuse the multiple features obtained from a same view, sensor or modality. Particularly, mapping matrices corresponding to a certain view are estimated to project the latent variable from a shared space to the multiple observations. Since this method is designed for the supervised purpose, we assume that the latent variables associated with different views are influenced by their ground-truth label. In order to effectively solve the proposed method, the Expectation-Maximization (EM) algorithm is applied to estimate the parameters and latent variables. Experimental results on the extensive synthetic and two real-world datasets substantiate the effectiveness and superiority of our approach as compared with state-of-the-art.
URLView the original
Language英語
Fulltext Access
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Hong Kong Polytechnic University
Recommended Citation
GB/T 7714
Li J.,Yong H.,Zhang B.,et al. A probabilistic hierarchical model for multi-view and multi-feature classification[C],2018:3498-3505.
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