A robust clustering algorithm based on aggregated heat kernel mapping

Hao Huang, Shinjae Yoo, Hong Qin, Dantong Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Current spectral clustering algorithms suffer from both sensitivity to scaling parameter selection in similarity matrix construction, and data perturbation. This paper aims to improve robustness in clustering algorithms and combat these two limitations based on heat kernel theory. Heat kernel can statistically depict traces of random walk, so it has an intrinsic connection with diffusion distance, with which we can ensure robustness during any clustering process. By integrating heat distributed along time scale, we propose a novel method called Aggregated Heat Kernel (AHK) to measure the distance between each point pair in their eigenspace. Using AHK and Laplace-Beltrami Normalization (LBN) we are able to apply an advanced noise-resisting robust spectral mapping to original dataset. Moreover it offers stability on scaling parameter tuning. Experimental results show that, compared to other popular spectral clustering methods, our algorithm can achieve robust clustering results on both synthetic and UCI real datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages270-279
Number of pages10
DOIs
StatePublished - Dec 1 2011
Externally publishedYes
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 14 2011

Other

Other11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/14/11

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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