近年来低秩表示和稀疏表示用于子空间聚类的研究得到了广泛关注, 文献中已有许多相关的子空间聚类方法. 文章结合弹性网正则化低秩表示和分类稀疏表示, 提出一种分类稀疏低秩表示的子空间聚类方法. 方法旨在更充分地捕获数据集的局部线性结构和全局结构信息, 提高聚类性能. 首先采用并行分裂的自适应惩罚的线性交替方向法求解模型, 然后利用求得的系数矩阵构造相似度矩阵, 最后应用谱聚类方法进行聚类. 另外, 取代现有方法手动调节正则化参数, 文章采用自适应调节正则化参数确定目标函数中各项的权重. 在人工数据集、Extended Yale B~数据库和~CMU PIE~数据库上的实验结果表明, 文章方法有更明显的聚类效果和更高的准确率.

Low-rank representation and sparse representation for subspace clustering have attracted much attention, and many related subspace clustering methods have been developed in recent years. In this paper, combining elastic net regularized low-rank representation and class-wise sparse representation, we propose a new subspace clustering method. The method aims to improve the clustering performance by fully capturing the local linear structure and global information of data sets. First, we solve the model by the linearized alternating direction method with parallel splitting and adaptive penalty, and then use the obtained coefficient matrix to build the affinity matrix. After that, the spectral clustering method is employed to cluster. In addition, we adopt an adaptive rule for the estimation of the regularization parameters instead of manually adjusting them in existing methods. Experimental results on the artificial data set, Extended Yale B database and CMU PIE database demonstrate the presented model brings more obvious clustering effect and higher accuracy than some existing models.