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《Acta Electronica Sinica》 2007-08
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Density-Sensitive Spectral Clustering

WANG Ling,BO Lie-feng,JIAO Li-cheng (Institute of Intelligent Information Processing,Xidian University,Xi'an,Shaanxi 710071,China)  
Spectral clustering has become increasingly popular in recent years.Being a pairwise method,the success of spectral clustering depends heavily on the choice of similarity measure.Through analyzing the property of data clusters,a novel data-dependent similarity measure is proposed,namely density-sensitive similarity measure,which has the ability of describing the characters of data clustering compared with the traditional Euclidian metric based similarity measure.Based on the novel similarity measure,we have a density-sensitive spectral clustering algorithm.Compared with the original spectral clustering,it has the advantages of effectively dealing with the multi-scale problems and relatively not sensitive to parameter.It obtains promising results not only on artificial datasets but also on USPS handwritten digit dataset.
【Fund】: 国家自然科学基金(No.60372050 60133010);; 国家863高技术研究发展计划(No.2002AA135080);; 国防预研项目(No.A1420060172)
【CateGory Index】: TP391.4
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