Robust Data Clustering in Mercer Kernel-Induced Feature Space

  • Xulei Yang
  • Qing Song
  • Meng-Joo Er
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, we focus on developing a new clustering method, robust kernel-based deterministic annealing (RKDA) algorithm, for data clustering in mercer kernel-induced feature space. A nonlinear version of the standard deterministic annealing (DA) algorithm is first constructed by means of a Gaussian kernel, which can reveal the structure in the data that may go unnoticed if DA is performed in the original input space. After that, a robust pruning method, the maximization of the mutual information against the constrained input data points, is performed to phase out noise and outliers. The good aspects of the proposed method for data clustering are supported by experimental results.


Mutual Information Gaussian Kernel Kernel Method Data Cluster Kernel Space 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xulei Yang
    • 1
  • Qing Song
    • 1
  • Meng-Joo Er
    • 1
  1. 1.EEE SchoolNanyang Technological UniversitySingapore

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