期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 20, 期 3, 页码 418-433出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2015.2476359
关键词
Constrained multiobjective evolutionary algorithm; similarity matrix; sparse representation; spectral clustering
资金
- National Basic Research Program (973 Program) of China [2013CB329402]
- Nature Inspired Computation and its Applications under EU FP7 IRSES Grant [247619]
- Major Research Plan of the National Natural Science Foundation of China [91438201]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT1170]
- Fundamental Research Funds for the Central Universities [K5051302028]
- Basque Government [IT609-13]
- Spanish Ministry of Economy and Competitiveness MINECO [TIN2013-41272-P]
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.
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