Dimensionality Reduction for Semi-supervised Face Recognition

  • Weiwei Du
  • Kohei Inoue
  • Kiichi Urahama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)


A dimensionality reduction technique is presented for semi-supervised face recognition where image data are mapped into a low dimensional space with a spectral method. A mapping of learning data is generalized to a new datum which is classified in the low dimensional space with the nearest neighbor rule. The same generalization is also devised for regularized regression methods which work in the original space without dimensionality reduction. It is shown with experiments that the spectral mapping method outperforms the regularized regression. A modification scheme for data similarity matrices on the basis of label information and a simple selection rule for data to be labeled are also devised.


Dimensionality Reduction Spectral Mapping Face Image Spectral Cluster Label Data 
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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  1. 1.
    Seeger, M.: Learning with labeled and unlabeled data. Tech. Reports, Edinburgh Univ (2001)Google Scholar
  2. 2.
    Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proc. ICML-2003, pp. 912–919 (2003)Google Scholar
  3. 3.
    Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: Proc. NIPS 2003(2003)Google Scholar
  4. 4.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Proc. NIPS 2001, pp. 849–856 (2001)Google Scholar
  5. 5.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comp. 15, 1373–1396 (2003)zbMATHCrossRefGoogle Scholar
  6. 6.
    Koren, Y.: On spectral graph drawing. In: Warnow, T.J., Zhu, B. (eds.) COCOON 2003. LNCS, vol. 2697, pp. 496–508. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Bengio, Y., Paiement, J.-F., Vincent, P.: Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps, and spectral clustering. In: Proc. NIPS 2003 (2003)Google Scholar
  8. 8.
    Dhillon, I.S., Modha, D.M.: Concept decompositions for large sparse text data using clustering. Mach. Learning 42, 143–175 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Weiwei Du
    • 1
  • Kohei Inoue
    • 1
  • Kiichi Urahama
    • 1
  1. 1.Kyushu UniversityFukuoka-shiJapan

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