Enhanced Balanced Min Cut

Abstract

Spectral clustering is a hot topic and many spectral clustering algorithms have been proposed. These algorithms usually solve the discrete cluster indicator matrix by relaxing the original problems, obtaining the continuous solution and finally obtaining a discrete solution that is close to the continuous solution. However, such methods often result in a non-optimal solution to the original problem since the different steps solve different problems. In this paper, we propose a novel spectral clustering method, named as Enhanced Balanced Min Cut (EBMC). In the new method, a new normalized cut model is proposed, in which a set of balance parameters are learned to capture the differences among different clusters. An iterative method with proved convergence is used to effectively solve the new model without eigendecomposition. Theoretical analysis reveals the connection between EBMC and the classical normalized cut. Extensive experimental results show the effectiveness and efficiency of our approach in comparison with the state-of-the-art methods.