Learning with privileged information using Bayesian networks

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• 作者：Shangfei Wang (1) (2)
  Menghua He (1) (2)
  Yachen Zhu (1) (2)
  Shan He (1) (2)
  Yue Liu (3)
  Qiang Ji (4)
  
  1. School of Computer Science and Technology ; University of Science and Technology of China ; Hefei ; 230027 ; China
  2. Key Lab of Computing and Communicating Software of Anhui Province ; Hefei ; 230027 ; China
  3. School of Mathematical Sciences ; University of Science and Technology of China ; Hefei ; 230027 ; China
  4. Department of Electrical ; Computer ; and Systems Engineering ; Rensselaer Polytechnic Institute ; Troy ; NY ; 12180-3590 ; USA
  
• 关键词：Bayesian network ; privileged information ; classification ; maximum likelihood estimation
• 刊名：Frontiers of Computer Science in China
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：9
• 期：2
• 页码：185-199
• 全文大小：581 KB
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• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

For many supervised learning applications, additional information, besides the labels, is often available during training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network (BN) approach for learning with privileged information. We propose to incorporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classification. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach.