Harmonic Fast One-Step Cut: An Efficient Strategy for Spectral Clustering Optimization.

Abstract

Due to the excellent performance of spectral clustering (SC), it has been widely used in many fields of application. However, the high computational complexity and two successive steps have limited SC's development. In addition, the traditional SC is formulated to maximize the arithmetic mean of trace ratios which is dominated by the larger objectives and may reduce the recognition accuracy in practical applications. In this article, we propose a novel graph cut criterion to minimize the trace ratios of harmonic mean with objectives, which can avoid the worst-cluster issue without imposing any regularization or constraints. Furthermore, an efficient and effective coordinate descent (CD) method is exploited to achieve a one-step solution. Therefore, this article can simultaneously solve three main challenges in a unified framework. Extensive experiments verify that the harmonic fast one-step graph cut (HFOC) achieves superior clustering performance with relatively less time-consuming compared to the other state-of-the-art clustering methods.