A Probabilistic Hierarchical Model for Multi-View and Multi-Feature Classification

Some 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 classi- fication. 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.

Authors

• Jinxing Li
• Hongwei Yong
• Bob Zhang Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Taipa, Macau
• Mu Li Harbin Institute of Technology, Shenzhen, China
• Lei Zhang
• David Zhang SRI International

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