Introducing global scaling parameters into Ncut

  • Authors:

  • Yiling Zeng;Hongbo Xu;Xueqi Cheng;Shuo Bai

  • Affiliations:

  • Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China and Shanghai Stock Exchange, Shanghai, China

  • Venue:
  • Proceedings of the 2010 ACM Symposium on Applied Computing
  • Year:
  • 2010

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Abstract

Gaussian similarity is usually used in spectral clustering. It generates the affinity matrix by mainly considering point-to-point distances in a local region with respect to the scaling parameters δ. As a result, global information is not considered. To address this problem, we design a mapping and rescaling framework (referred as M-R framework) to introduce global scaling parameters into spectral clustering. The M-R framework is applied on Normalized Cut to form the M-R Ncut algorithm which obtains remarkable performance improvements in our experimental evaluations.