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 algorith...

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Bibliographic Details
Main Authors: Li-Feng Liu, Yan-Yun Qu, Cui-Hua Li, Yuan Xie
Format: Conference Proceeding
Language:English
Subjects:
Application software
Clustering algorithms
Clustering methods
Computational intelligence
Computer industry
Computer science
Convergence
Dimension Reduction
Eigenvalues and eigenfunctions
Particle swarm optimization
PSO
Spectral Clustering
Videoconference
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Summary: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.
DOI:10.1109/PACIIA.2009.5406456