Semi-supervised classification with privileged information

详细信息    查看全文

• 作者：Zhiquan Qi ; Yingjie Tian ; Lingfeng Niu…
• 关键词：Classification ; Support vector machine ; Privileged information
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：6
• 期：4
• 页码：667-676
• 全文大小：1,176 KB
• 参考文献：1.Vapnik V, Vashist A (2009) A new learning paradigm: learning using privileged information. Neural Netw 22(5-):544-57View Article
  2.Vapnik V (1995) The nature of statistical learning theory. Springer, New York
  3.Vapnik V (1996) The nature of statistical learning theory. Springer, New York
  4.Vapnik V (2006) Estimation of dependences based on empirical data (information science and statistics). Springer, Berlin
  5.Pechyony D, Vapnik V (2010) On the theory of learning with privileged information. In: Advances in neural information processing systems, vol 23
  6.Pechyony D, Izmailov R, Vashist A, Vapnik V (2010) Smo-style algorithms for learning using privileged information. In: DMIN. CSREA Press, Providence, pp 235-41
  7.Seeger M (2001) Learning with labeled and unlabeled data. Technical report
  8.Chapelle O, Sch?lkopf B, Zien A (eds) (2006) Semi-supervised learning (adaptive computation and machine learning). The MIT Press, Cambridge
  9.Zhu X (2006) Semi-supervised learning literature survey. Technical Report 15304, University of Wisconsin, Madison
  10.Belkin M, Matveeva I, Niyogi P (2004) Regularization and semi-supervised learning on large graphs. In: COLT. Springer, Berlin, pp 624-38
  11.Grandvalet Y, Bengio Y (2005) Semi-supervised learning by entropy minimization. In: CAP, PUG, pp 281-96
  12.Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399-434MathSciNet
  13.Joachims T (2003) Transductive learning via spectral graph partitioning. In: ICML, pp 290-97
  14.Belkin M, Niyogi P (2002) Using manifold structure for partially labelled classification. In: NIPS, pp 953-60
  15.Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, pp 912-19
  16.Deng N, Tian Y, Zhang C (2011) Optimization based data mining: theory and applications. Springer Press, Berlin
  17.Tian Y, Yong S, Xiaohui L (2012) Recent advances on support vector machines research. Technol Econ Dev Econ 18(1): 5-3
  18.Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Science 197(4300):287-89View Article
  19.Everingham M, Zisserman A, Williams CKI, Van Gool L (2006) The PASCAL visual object classes challenge 2006 (VOC 2006) results. http://?www.?pascal-network.?org/?challenges/?VOC/?voc2006/?results.?pdf
  20.Deng Y, Manjunath BS, Kenney C, Moore MS, Member S, Shin H (2001) An efficient color representation for image retrieval. IEEE Trans Image Process 10:140-47View Article
  
• 作者单位：Zhiquan Qi (1)
  Yingjie Tian (1)
  Lingfeng Niu (1)
  Bo Wang (1)
  
  1. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China
  
• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Statistical Physics, Dynamical Systems and Complexity
  Computational Intelligence
  Control , Robotics, Mechatronics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-808X

文摘

The privileged information that is available only for the training examples and not available for test examples, is a new concept proposed by Vapnik and Vashist (Neural Netw 22(5-):544-57, 2009). With the help of the privileged information, learning using privileged information (LUPI) (Neural Netw 22(5-):544-57, 2009) can significantly accelerate the speed of learning. However, LUPI is a standard supervised learning method. In fact, in many real-world problems, there are also a lot of unlabeled data. This drives us to solve problems under a semi-supervised learning framework. In this paper, we propose a semi-supervised learning using privileged information (called Semi-LUPI), which can exploit both the distribution information in unlabeled data and privileged information to improve the efficiency of the learning. Furthermore, we also compare the relative importance of both types of information for the learning model. All experiments verify the effectiveness of the proposed method, and simultaneously show that Semi-LUPI can obtain superior performances over traditional supervised and semi-supervised methods.