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Abstract

Recently Multiple Kernel Learning (MKL) has gained increasing attention in constructing a combinational kernel from a number of basis kernels. In this paper, we proposed a novel approach of multiple kernel learning for clustering based on the kernel k-means algorithm. Rather than using a convex combination of multiple kernels over the whole input space, our method associates to each cluster a localized kernel. We assign to each cluster a weight vector for feature selection and combine it with a Gaussian kernel to form a unique kernel for the corresponding cluster. A locally adaptive strategy is used to localize the kernel for each cluster with the aim of minimizing the within-cluster variance of the corresponding cluster. We experimentally compared our methods to kernel k-means and spectral clustering on several data sets. Empirical results demonstrate the effectiveness of our method.

Keywords

kernel methods kernel k-means multiple kernel clustering localized kernel 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lujiang Zhang
    • 1
  • Xiaohui Hu
    • 1
    • 2
  1. 1.School of Automation Science and Electrical EngineeringBeijing University of Aeronautics & AstronauticsBeijingChina
  2. 2.Institute of SoftwareChinese Academy of SciencesBeijingChina

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