RSSL: Semi-supervised Learning in R

  • Jesse H. Krijthe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10214)


In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes and comment on their use and implementation. We then show, using several code examples, how the package can be used to replicate well-known results from the semi-supervised learning literature.


Semi-supervised learning Reproducibility Pattern recognition 



This work was funded by project P23 of the Dutch public/private research network COMMIT.


  1. 1.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)CrossRefGoogle Scholar
  3. 3.
    Collobert, R., Sinz, F., Weston, J., Bottou, L.: Large scale transductive SVMs. J. Mach. Learn. Res. 7, 1687–1712 (2006)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Cozman, F.G., Cohen, I., Cirelo, M.C.: Semi-supervised learning of mixture models. In: Proceedings of the 20th International Conference on Machine Learning, pp. 99–106 (2003)Google Scholar
  5. 5.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Eddelbuettel, D., Francois, R.: Rcpp: seamless R and C++ Integration. J. Stat. Softw. 40(1), 1–18 (2011)Google Scholar
  7. 7.
    Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 529–536. MIT Press, Cambridge (2005)Google Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning, 2nd edn. Springer, New York (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Joachims, T.: Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning, pp. 200–209. Morgan Kaufmann Publishers, San Francisco (1999)Google Scholar
  10. 10.
    Krijthe, J.H., Loog, M.: Implicitly constrained semi-supervised linear discriminant analysis. In: Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, pp. 3762–3767 (2014)Google Scholar
  11. 11.
    Krijthe, J.H., Loog, M.: Optimistic semi-supervised least squares classification. In: Proceedings of the 23rd International Conference on Pattern Recognition (2016)Google Scholar
  12. 12.
    Krijthe, J.H., Loog, M.: Projected estimators for robust semi-supervised classification. Mach. Learn. (to appear, 2017).
  13. 13.
    Krijthe, J.H., Loog, M.: Robust semi-supervised least squares classification by implicit constraints. Pattern Recogn. 63, 115–126 (2017)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Tsang, I., Kwok, J., Zhou, Z.: Convex and scalable weakly labeled SVMs. J. Mach. Learn. Res. 14, 2151–2188 (2013).
  15. 15.
    Li, Y.F., Zhou, Z.H.: Towards making unlabeled data never hurt. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 175–188 (2015)CrossRefGoogle Scholar
  16. 16.
    Loog, M.: Constrained parameter estimation for semi-supervised learning: the case of the nearest mean classifier. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6322, pp. 291–304. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15883-4_19 CrossRefGoogle Scholar
  17. 17.
    Loog, M.: Semi-supervised linear discriminant analysis through moment-constraint parameter estimation. Pattern Recogn. Lett. 37, 24–31 (2014)CrossRefGoogle Scholar
  18. 18.
    Loog, M.: Contrastive pessimistic likelihood estimation for semi-supervised classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 462–475 (2016)CrossRefGoogle Scholar
  19. 19.
    Loog, M., Jensen, A.C.: Semi-supervised nearest mean classification through a constrained log-likelihood. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 995–1006 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    McLachlan, G.J.: Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis. J. Am. Stat. Assoc. 70(350), 365–369 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  22. 22.
    R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2016).
  23. 23.
    Shaffer, J.P.: The Gauss-Markov theorem and random regressors. Am. Stat. 45(4), 269–273 (1991)MathSciNetGoogle Scholar
  24. 24.
    Sindhwani, V., Keerthi, S.S.: Large scale semi-supervised linear SVMs. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 477–484. ACM (2006)Google Scholar
  25. 25.
    Webb, A.: Statistical Pattern Recognition, 2nd edn. John Wiley, New York (2002)CrossRefzbMATHGoogle Scholar
  26. 26.
    Wickham, H.: ggplot2: Elegant Graphics for Data Analysis. Springer, New York (2009). CrossRefzbMATHGoogle Scholar
  27. 27.
    Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning, pp. 912–919 (2003)Google Scholar
  28. 28.
    Zhu, X., Goldberg, A.B.: Introduction to Semi-supervised Learning. Morgan & Claypool, San Rafael (2009)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Pattern Recognition LaboratoryDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of Molecular EpidemiologyLeiden University Medical CenterLeidenThe Netherlands

Personalised recommendations