Automatic Spectral Clustering and its Application

Abstract

An new algorithm called automatic spectral clustering (ASC) is proposed based on eigengap and orthogonal eigenvector in this paper. It mainly focuses on how to automatically determine the suitable class number in clustering and explores some intrinsic characteristics of the spectral clustering method. The proposed method firstly constructs the affinity matrix of data and carries on eigen-decomposition, then determine the class number according to the eigengap. Finally, the data are classified by employing the angle between two eigenvectors. The experiments on the real-world data sets from UCI and applications in face location show the correctness and efficiency of the proposed method.