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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 eigendecomposition, and then a multi-class clustering problem should 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.