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Iterative Laplacian Score for Feature Selection

  • Linling Zhu
  • Linsong Miao
  • Daoqiang Zhang
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

Laplacian Score (LS) is a popular feature ranking based feature selection method both supervised and unsupervised. In this paper, we propose an improved LS method called Iterative Laplacian Score (IterativeLS), based on iteratively updating the nearest neighborhood graph for evaluating the importance of a feature by its locality preserving ability. Compared with LS, the key idea of IterativeLS is to gradually improve the nearest neighbor graph by discarding the least relevant features at each iteration. Experimental results on several high dimensional data sets demonstrate the effectiveness of our proposed method.

Keywords

feature selection Iterative Laplacian score Laplacian score locality preserving 

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References

  1. 1.
    Belkin, M., Niyogi, P.: Laplacian Egienmaps and Spectral Techniques for Embedding and Clustering. In: Advances in Neural Information Processing System, vol. 14 (2001)Google Scholar
  2. 2.
    Dy, J.G., Brodley, C.E., Kak, A.C., Broderick, L.S., Aisen, A.M.: Unsupervised feature selection applied to content-based retrieval of lung image. IEEE Trans. Pattern Anal. Mach Intell. 25, 373–378 (2003)CrossRefGoogle Scholar
  3. 3.
    Dy, J.G., Brodley, C.E.: Feature selection for unsupervised feature learning. J. March. Learning Res. 5, 845–889 (2004)MathSciNetzbMATHGoogle Scholar
  4. 4.
    He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing System, vol. 17. MIT press, Cambridge (2005)Google Scholar
  5. 5.
    He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing System, vol. 16. MIT Press, Cambridge (2004)Google Scholar
  6. 6.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  7. 7.
    Xu, W., Liu, X., Gong, Y.: Document Clustering Based on Non-negative Matrix Factorization. In: ACM SIGIR Conference on Information Retrieval (2003)Google Scholar
  8. 8.
    Sun, Y., Todorovic, S., Goodison, S.: Local-learning-Based Feature Selection for High-Dimensional Data Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligent 32(9) (2010)Google Scholar
  9. 9.
    Xu, J., Man, H.: Dictionary Learning Based on Laplacian Score in Sparse Coding. In: Perner, P. (ed.) MLDM 2011. LNCS (LNAI), vol. 6871, pp. 253–264. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Linling Zhu
    • 1
  • Linsong Miao
    • 1
  • Daoqiang Zhang
    • 1
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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