Abstract：The clustering result is determined by the quality of the eigenvectors (spectral) of the related graph Laplacian in spectral clustering, and therefore eigenvector selection is crucial. To solve this problem, spectral discrimination(SD), spectral discrimination availability(SDA) and spectral discrimination degree(SDD) are defined based on generalized information entropy. SD is exploited to measure the clustering information of each eigenvector. SDA is utilized to remove the ineffective eigenvectors for clustering. SDD is employed to construct a selective clustering ensemble scheme based on contribution for clustering. Thus, a spectral clustering algorithm based on eigenvector selection is proposed. The experimental results on varied natural images show that the proposed algorithm is simple and effective.
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2019, Vol. 32 
