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A Graph Based Subspace Semi-supervised Learning Framework for Dimensionality Reduction

  • Wuyi Yang
  • Shuwu Zhang
  • Wei Liang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)

Abstract

The key to the graph based semi-supervised learning algorithms for classification problems is how to construct the weight matrix of the p-nearest neighbor graph. A new method to construct the weight matrix is proposed and a graph based Subspace Semi-supervised Learning Framework (SSLF) is developed. The Framework aims to find an embedding transformation which respects the discriminant structure inferred from the labeled data, as well as the intrinsic geometrical structure inferred from both the labeled and unlabeled data. By utilizing this framework as a tool, we drive three semi-supervised dimensionality reduction algorithms: Subspace Semi-supervised Linear Discriminant Analysis (SSLDA), Subspace Semi-supervised Locality Preserving Projection (SSLPP), and Subspace Semi-supervised Marginal Fisher Analysis (SSMFA). The experimental results on face recognition demonstrate our subspace semi-supervised algorithms are able to use unlabeled samples effectively.

Keywords

Dimensionality Reduction Face Recognition Linear Discriminant Analysis Unlabeled Data Constraint Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)zbMATHGoogle Scholar
  2. 2.
    He, X., Yan, S., Zhang, Y.H.P.N., Face, H.J.: recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)CrossRefGoogle Scholar
  3. 3.
    Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., Lin, S.: Graph embedding and extension: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007)CrossRefGoogle Scholar
  4. 4.
    Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: 20th International Conference on Machine Learning (2003)Google Scholar
  5. 5.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from examples. Journal of Machine Learning Research 7(11), 2399–2434 (2006)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Sindhwani, V., Niyogi, P., Belkin, M.: Beyond the point cloud: from transductive to semi-supervised learning. In: ICML 2005, 22nd International Conference on Machine Learning (2005)Google Scholar
  7. 7.
    Zhou, D., Bousquet, O., Lal, T., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems (2003)Google Scholar
  8. 8.
    Deng Cai, X.H., Han, J.: Semi-supervised discriminant analysis. In: IEEE International Conference on Computer Vision (2007)Google Scholar
  9. 9.
    Tenenbaum, J., de Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  10. 10.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2002)Google Scholar
  11. 11.
    Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  12. 12.
    Sim, T., Baker, S., Bsat, M.: The cmu pose, illuminlation, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wuyi Yang
    • 1
  • Shuwu Zhang
    • 1
  • Wei Liang
    • 1
  1. 1.Digital Content Technology Research Center, Institute of AutomationChinese Academy of SciencesBeijingChina

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