Coefficient Enhanced Least Square Regression Subspace Clustering Method

doi:10.3778/j.issn.1002-8331.2103-0480

Abstract

Abstract: In view of the fact that the least square regression subspace clustering method ignores the similarity between samples when solving the representation coefficients, an improved method is proposed. Based on the representation coefficient matrix of the sample mutual reconstruction and the similarity matrix of the sample having a great correlation, it defines the coefficient enhancement term to solve the representation coefficient matrix that can preserve sample similarity. Experiments on 8 standard data sets show that the proposed method can improve the performance of least square regression subspace clustering method.

Key words: least square regression, subspace clustering, coefficient enhanced, high dimensional data

摘要： 针对最小二乘回归子空间聚类法在求解表示系数时忽略了样本相似度的不足，提出改进方法。基于样本相互重构的表示系数矩阵和样本相似度矩阵有很大的关联定义系数增强项，求解可以保持样本相似度的表示系数矩阵，提出系数增强最小二乘回归子空间聚类法。在8个标准数据集上的实验表明该方法可以提高最小二乘回归子空间聚类法的聚类性能。

关键词: 最小二乘回归, 子空间聚类, 系数增强, 高维数据

JIAN Cairen, WENG Qian, XIA Jingbo. Coefficient Enhanced Least Square Regression Subspace Clustering Method[J]. Computer Engineering and Applications, 2022, 58(20): 73-78.

简彩仁, 翁谦, 夏靖波. 系数增强最小二乘回归子空间聚类法[J]. 计算机工程与应用, 2022, 58(20): 73-78.

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