Multi-class spectral clustering based on particle swarm optimization

Spectral clustering has been used in computer vision successfully in recent years, which refers to the algorithm that the global optima is found in the relaxed continuous domain obtained by eigen decomposition, and then a multi-class clustering problem should be solved by traditional clustering algorithm such as k-means. In this paper, we propose a novel spectral clustering algorithm based on particle swarm optimization (PSO). The major contribution of this work is to combine PSO technique with spectral clustering. In the multi-class clustering stage, the PSO is applied in the feature space to cluster the new data, each of which is a characterization of the original data. Experimental studies on PSO-based spectral clustering algorithm demonstrate that the proposed algorithm provides global convergence, steady performance and better accuracy.