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Manifold Learner Ensemble

  • Peng Zhang
  • Chunbo Fan
  • Yuanyuan Ren
  • Nina Zhang
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

Manifold learning is proved to be an efficient tool for nonlinear dimensionality reduction. Various local and global learners have been proposed to successfully extract intrinsic geometry underlying high-dimensional data cloud. However, there is no work considering the ensemble of local and global manifold learners to promote learning results, where such strategy has achieved great success in classification. In this paper, we propose a manifold learner ensemble method (MLEN) for the first time. MLEN consists of a local manifold learner and a global one. By fusing the extracted local and global geometry, MLEN outperforms each of its components and outputs an overall and superior embedding. Experimental results on both synthetic and image manifolds validate the effectiveness of the proposed method.

Keywords

manifold learning ensemble learning dimensionality reduction feature extraction 

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References

  1. 1.
    Absil, P.A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2007)Google Scholar
  2. 2.
    Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36, 105–142 (1999)CrossRefGoogle Scholar
  3. 3.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)zbMATHCrossRefGoogle Scholar
  4. 4.
    Chen, L., Buja, A.: Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis. Journal of the American Statitical Association 104(485), 209–219 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Domingos, P.: A few useful things to know about machine learning. Communications of the ACM 55(10), 78–87 (2012)CrossRefGoogle Scholar
  6. 6.
    Lin, T., Zha, H.: Riemannian manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(5), 796–809 (2008)CrossRefGoogle Scholar
  7. 7.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  8. 8.
    Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  9. 9.
    Weinberger, K., Saul, L.: Unsupervised learning of image manifolds by semidefinite programming. International Journal of Computer Vision 70(1), 77–90 (2006)CrossRefGoogle Scholar
  10. 10.
    Zhang, P., Ren, Y., Zhang, B.: A new embedding quality assessment method for manifold learning. Neurocomputing 97, 251–266 (2012)CrossRefGoogle Scholar
  11. 11.
    Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM Journal on Scientific Computing 26(1), 313–338 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peng Zhang
    • 1
  • Chunbo Fan
    • 1
  • Yuanyuan Ren
    • 2
  • Nina Zhang
    • 1
  1. 1.Data CenterNational Disaster Reduction Center of ChinaBeijingP.R. China
  2. 2.Institute of MicrobiologyChinese Academy of SciencesBeijingP.R. China

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