Advertisement

Multiview Subspace Learning

  • Shiliang SunEmail author
  • Liang Mao
  • Ziang Dong
  • Lidan Wu
Chapter

Abstract

In multiview settings, observations from different views are assumed to share the same subspace. The abundance of views can be utilized to better explore the subspace. In this chapter, we consider two different kinds of multiview subspace learning problems. The first one contains the general unsupervised multiview subspace learning problems. We focus on canonical correlation analysis as well as some of its extensions. The second one contains the supervised multiview subspace learning problems, i.e., there exists available label information. In this case, representations more suitable for the on-hand task can be obtained by utilizing the label information. We also briefly introduce some other methods at the end of this chapter.

References

  1. Bach FR, Jordan MI (2005) A probabilistic interpretation of canonical correlation analysis, pp 1–11. http://statistics.berkeley.edu/sites/default/files/tech-reports/688
  2. Borga M (2001) Canonical correlation: a tutorial, pp 1–12. http://people.imt.liu.se/magnus/cca
  3. Chen Q, Sun S (2009) Hierarchical multi-view fisher discriminant analysis. In: Proceedings of the 16th international conference on neural information processing: Part II, pp 289–298CrossRefGoogle Scholar
  4. Diethe T, Hardoon DR, Shawe-Taylor J (2008) Multiview fisher discriminant analysis. In: NIPS workshop on learning from multiple sourcesGoogle Scholar
  5. Hardoon DR, Szedmak SR, Shawe-taylor JR (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefGoogle Scholar
  6. Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):498–520CrossRefGoogle Scholar
  7. Horst P (1961) Relations amongm sets of measures. Psychometrika 26(2):129–149MathSciNetCrossRefGoogle Scholar
  8. Hofmann T, Schölkopf B, Smola AJ (2008) Kernel methods in machine learning. Ann Stat 1171–1220MathSciNetCrossRefGoogle Scholar
  9. Jin Z, Yang JY, Hu ZS, Lou Z (2001) Face recognition based on the uncorrelated discriminant transformation. Pattern Recognit 34(7):1405–1416CrossRefGoogle Scholar
  10. Klami A, Virtanen S, Kaski S (2013) Bayesian canonical correlation analysis. J Mach Learn Res 14(1):965–1003MathSciNetzbMATHGoogle Scholar
  11. Lai PL, Fyfe C (2000) Kernel and nonlinear canonical correlation analysis. Int J Neural Syst 10(5):365–377CrossRefGoogle Scholar
  12. Luo Y, Tao D, Ramamohanarao K, Xu C, Wen Y (2015) Tensor canonical correlation analysis for multi-view dimension reduction. IEEE Trans Knowl Data Eng 27(11):3111–3124CrossRefGoogle Scholar
  13. Rupnik J (2010) Multi-view canonical correlation analysis. Technical reportGoogle Scholar
  14. Sun S, Chen Q (2011) Hierarchical distance metric learning for large margin nearest neighborhood classification. Int J Pattern Recognit Artif Intell 25(7):1073–1087MathSciNetCrossRefGoogle Scholar
  15. Xu C, Tao D, Xu C (2015) Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell 37(12):2531–2544CrossRefGoogle Scholar
  16. Yang M, Sun S (2014) Multi-view uncorrelated linear discriminant analysis with applications to handwritten digit recognition. In: Proceedings of 2014 international joint conference on neural networks, pp 4175–4181Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

Personalised recommendations