Abstract
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.
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Shangfei Wang received her BS in electronic engineering from Anhui University, China, in 1996. She received her MS in circuits and systems, and the PhD in signal and information processing from University of Science and Technology of China (USTC), China in 1999 and 2002. From 2004 to 2005, she was a postdoctoral research fellow in Kyushu University, Japan. Between 2011 and 2012, Dr. Wang was a visiting scholar at Rensselaer Polytechnic Institute in Troy, NY, USA. She is currently an Associate Professor of School of Computer Science and Technology, USTC. Dr. Wang is an IEEE and ACM member. Her research interests cover computation intelligence, affective computing, and probabilistic graphical models. She has authored or co-authored over 70 publications.
Menghua He received her BS in information and computation science from Anhui University, China in 2011. She is currently pursuing her MS in computer science at the University of Science and Technology of China (USTC), China. Her research interesting is affective computing.
Yachen Zhu received his BS in computer science from University of Science and Technology of China, China in 2010. He is currently pursuing the PhD degree in computer science in the University of Science and Technology of China (USTC), China. His research interest is affective computing.
Shan He received his BS in computer science from Anhui Agriculture University, China in 2010. He received his MS in Computer Science in the University of Science and Technology of China (USTC), China in 2013. He is currently a researcher of Iflytek research.
Yue Liu is currently pursuing her BS in School of Mathematical Sciences from University of Science and Technology of China (USTC), China. She is the monitor of her class. She achieves scholarships for incoming freshmen and honor of outstanding class leader in USTC. Her research interest is affective computing.
Qiang Ji received his PhD in electrical engineering from the University of Washington, USA. He is currently a Professor with the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), USA. He recently served as a program director at the National Science Foundation (NSF), where he managed NSF’s computer vision and machine learning programs. He also held teaching and research positions with the Beckman Institute at University of Illinois at Urbana-Champaign, the Robotics Institute at Carnegie Mellon University, the Dept. of Computer Science at University of Nevada at Reno, and the US Air Force Research Laboratory. Prof. Ji currently serves as the director of the Intelligent Systems Laboratory (ISL) at RPI.
Prof. Ji’s research interests are in computer vision, probabilistic graphical models, information fusion, and their applications in various fields. He has published over 160 papers in peer-reviewed journals and conferences. His research has been supported by major governmental agencies including NSF, NIH, DARPA, ONR, ARO, and AFOSR as well as by major companies including Honda and Boeing. Prof. Ji is an editor on several related IEEE and international journals and he has served as a general chair, program chair, technical area chair, and program committee member in numerous international conferences/workshops. Prof. Ji is a fellow of IAPR.
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Wang, S., He, M., Zhu, Y. et al. Learning with privileged information using Bayesian networks. Front. Comput. Sci. 9, 185–199 (2015). https://doi.org/10.1007/s11704-014-4031-8
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DOI: https://doi.org/10.1007/s11704-014-4031-8