A New Anticorrelation-Based Spectral Clustering Formulation

This paper introduces the Spectral Clustering Equivalence (SCE) algorithm which is intended to be an alternative to spectral clustering (SC) with the objective to improve both speed and quality of segmentation. Instead of solving for the spectral decomposition of a similarity matrix as in SC, SCE converts the similarity matrix to a columncentered dissimilarity matrix and searches for a pair of the most anticorrelated columns. The orthogonal complement to these columns is then used to create an output feature vector (analogous to eigenvectors obtained via SC), which is used to partition the data into discrete clusters. We demonstrate the performance of SCE on a number of artificial and real datasets by comparing its classification and image segmentation results with those returned by kernel-PCA and Normalized Cuts algorithm. The column-wise processing allows the applicability of SCE to Very Large Scale problems and asymmetric datasets.