Abstract For a K clustering problem, Ng-Jordan-Weiss (NJW) spectral clustering method adopts the eigenvectors corresponding to the K largest eigenvalues of the normalized affinity matrix derived from a dataset as a novel representation of the original data. However, these K eigenvectors can not always reflect the structure of the original data for some pattern recognition problems. In this paper, a semi-supervised eigenvector selection method for spectral clustering is proposed. This method utilizes some amount of supervised information to search the eigenvector combination which can reflect the structure of the original data, and then obtains more satisfying performance than the classical spectral clustering algorithms. Experimental results on UCI benchmark datasets and MNIST handwritten digits datasets show that the proposed method is effective and robust.
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2011, Vol. 24 
