NDLI: A Robust Clustering Algorithm Based on Aggregated Heat Kernel Mapping

Content Provider	IEEE Xplore Digital Library
Author	Hao Huang Shinjae Yoo Hong Qin Dantong Yu
Copyright Year	2011
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 eigen space. 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.
Starting Page	270
Ending Page	279
File Size	435510
Page Count	10
File Format	PDF
ISBN	9781457720758
ISSN	15504786
DOI	10.1109/ICDM.2011.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heating Clustering algorithms Kernel Robustness Laplace equations Sensitivity Noise Green's function methods Spectral analysis Diffusion processes
Content Type	Text
Resource Type	Article

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