Feature Selection in Spectral Clustering

نویسنده:
, , ,
سال
: 2011
چکیده: Spectral clustering is a powerful technique in clustering specially when the structure of

data is not linear and classical clustering methods lead to fail. In this paper, we propose a

spectral clustering algorithm with a feature selection schema based on extracted features of

Kernel PCA. In the proposed algorithm, selecting appropriate vectors is dependent upon

entropy of clusters on these vectors and weighting method is influenced by sum of the

existence gap between clusters and entropy of the vectors. Tuning the parameters has a great

effect on the results of spectral clustering techniques. In the ideal case, comparing our

method with NJW and Kernel K-Means indicate the successful of the proposed algorithm.
کلیدواژه(گان): spectral clustering,kernel PCA,feature selection,entropy
کالکشن :
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