Microarray Design Using the Hilbert–Schmidt Independence Criterion

  • Justin Bedo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)


This paper explores the design problem of selecting a small subset of clones from a large pool for creation of a microarray plate. A new kernel based unsupervised feature selection method using the Hilbert–Schmidt independence criterion (hsic) is presented and evaluated on three microarray datasets: the Alon colon cancer dataset, the van ’t Veer breast cancer dataset, and a multiclass cancer of unknown primary dataset. The experiments show that subsets selected by the hsic resulted in equivalent or better performance than supervised feature selection, with the added benefit that the subsets are not target specific.


Feature Selection Feature Subset Linear Kernel Full Dataset Polynomial Kernel 
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 2008

Authors and Affiliations

  • Justin Bedo
    • 1
  1. 1.The Australian National University, NICTA, and the University of MelbourneAustralia

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