Abstract：Not all of the top eigenvectors contain clustering information for the task of realworld data clustering. Since the noise exists, the distribution of elements of an eigenvector is complex and it is necessary to select eigenvectors for spectral clustering. In this paper, the integrated squared error (ISE) divergence is generalized and the proposed generalized integrated squared error (GISE) is used to estimate the multimodality of data distribution and measure the clustering information of eigenvector. Then, a spectral clustering algorithm based on eigenvector selection is proposed. The experimental results on varied natural images segmentation show that the proposed algorithm is simpler and more effective than pervious algorithms.
