Kernel k-means: spectral clustering and normalized cuts

  title={Kernel k-means: spectral clustering and normalized cuts},
  author={Inderjit S. Dhillon and Yuqiang Guan and Brian Kulis},
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have remained only loosely related. In this paper, we give an explicit theoretical connection between them. We show the generality of the weighted kernel k-means objective function, and derive the spectral clustering objective of normalized cut as a special case. Given a positive definite similarity matrix, our results lead… CONTINUE READING
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