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)


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.


feature selection Iterative Laplacian score Laplacian score locality preserving 


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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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