The performance of self-representation based methods is affected by redundant high-dimensional features. Therefore, a subspace clustering method via joint feature selection and smooth representation( FSSR) is proposed in this paper. Firstly, the idea of feature selection is integrated into the self-representation based coefficient matrix learning framework. Meanwhile, a weight factor is adopted to measure different contributions of correlated features. Furthermore, a group effectiveness constraint is imposed on the coefficient matrix for the preservation of locality property. An alternating direction method of multipliers( ADMM) based algorithm is derived to optimize the proposed cost function. Experiments are conducted on synthetic data and standard databases and the results demonstrate that FSSR outperforms the state-of-the-art approaches in both accuracy and efficiency.%基于自表示关联图的谱聚类模型性能受冗余特征影响较大.为了缓解高维数据无效特征的负面影响,文中提出联合特征选择和光滑表示的子空间聚类算法.首先基于自表示思想构建系数矩阵,将特征选择与数据重构纳入同一框架,同时使用权值因子衡量相关特征贡献度,并对系数矩阵进行组效应约束以保持局部性.通过交替变量更新法优化目标函数模型.在人造数据与标准数据库上的实验表明,文中算法在各项性能上均较优.
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