局部约束加强的最小二乘回归子空间聚类

摘要

Least square regression subspace clustering ( LSR ) is the lack of local correlation information of data, and thus dense representation is caused. Aiming at this problem, locality constraint enhanced least squares regression subspace clustering( LC LSR) is proposed. The original algorithm of LSR is extended by adding the local correlation constraint to achieve an accurate coefficient matrix and then it is close to being block diagonal. Furthermore, a method to construct affinity matrix is proposed. The proposed algorithm can better strengthen the affinities within each cluster and weaken the ones across clusters. Experimental results show that the proposed algorithm effectively improves the accuracy of clustering and its effectiveness and feasibility are verified.%针对最小二乘回归子空间聚类算法存在的数据局部相关性信息缺失、系数矩阵稀疏性不足的缺点,提出局部约束加强的最小二乘回归子空间聚类算法.在原始的最小二乘回归子空间聚类算法的基础上加入数据局部相关性约束,使表示系数矩阵的块对角性质更明显.同时,提出相似度矩阵构造方法,有效提高类内相似度,降低类间相似度.实验表明文中算法可以有效提高聚类的精确度,从而验证算法有效可行.