Kernel methods for point symmetry-based clustering
作者：
Highlights：
• Generalization (by kernelization) of a family of point symmetry distances.
• Kernelized-SBKM that offers new possibilities for point symmetry-based clustering.
• Empirical recognition of symmetric clusters using any proximity measure.
• Highlighting new simple examples, hard to manage by original methods.
• New complex examples well-managed with KSBKM by using implicit projections.
摘要
Highlights•Generalization (by kernelization) of a family of point symmetry distances.•Kernelized-SBKM that offers new possibilities for point symmetry-based clustering.•Empirical recognition of symmetric clusters using any proximity measure.•Highlighting new simple examples, hard to manage by original methods.•New complex examples well-managed with KSBKM by using implicit projections.
论文关键词：Pattern recognition,Clustering,Point symmetry-based distance measure,Kernel function,K-means
论文评审过程：Received 12 August 2014, Revised 15 February 2015, Accepted 16 March 2015, Available online 24 March 2015, Version of Record 16 May 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.03.013
