A Sparse Spectral Clustering Framework via Multiobjective Evolutionary Algorithm

作者：Luo, Juanjuan^*; Jiao, Licheng; Lozano, Jose A.

来源：IEEE Transactions on Evolutionary Computation, 2016, 20(3): 418-433.

DOI：10.1109/TEVC.2015.2476359

• Constrained multiobjective evolutionary algorithm
• similarity matrix
• sparse representation
• spectral clustering

This paper introduces sparse representation into spectral clustering and provides a sparse spectral clustering framework via a multiobjective evolutionary algorithm. In contrast to conventional spectral clustering, the main contribution of this paper is to construct the similarity matrix using a sparse representation approach by modeling spectral clustering as a constrained multiobjective optimization problem. Specific operators are designed to obtain a set of high quality solutions in the optimization process. Furthermore, we design a method to select a tradeoff solution from the Pareto front using a measurement called ratio cut based on an adjacency matrix constructed by all the nondominated solutions. We also extend the framework to the semi-supervised clustering field by using the semi-supervised information brought by the labeled samples to set some constraints or to guide the searching process. Experiments on commonly used datasets show that our approach outperforms four well-known similarity matrix construction methods in spectral clustering, and one multiobjective clustering algorithm. A practical application in image segmentation also demonstrates the efficiency of the proposed algorithm.

• 出版日期2016-6
• 单位西安电子科技大学