A Sparse Spectral Clustering Framework via Multiobjective Evolutionary Algorithm

doi:10.1109/TEVC.2015.2476359

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A Sparse Spectral Clustering Framework via Multiobjective Evolutionary Algorithm

2016-06-01

PRE

Juanjuan Luo *

Licheng Jiao

José A. Lozano

DOI：10.1109/TEVC.2015.2476359

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Abstract

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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.

Keywords:

Constrained multiobjective evolutionary algorithm

similarity matrix

sparse representation

spectral clustering

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