聚类分析
数学
子空间拓扑
图形
约束聚类
回归
规范(哲学)
计算机科学
人工智能
相关聚类
模式识别(心理学)
数据挖掘
算法
CURE数据聚类算法
理论计算机科学
统计
法学
政治学
作者
Yongyong Chen,Shuqin Wang,Feifei Zheng,Yigang Cen
标识
DOI:10.1016/j.knosys.2020.105482
摘要
Many works have proven that the consistency and differences in multi-view subspace clustering make the clustering results better than the single-view clustering. Therefore, this paper studies the multi-view clustering problem, which aims to divide data points into several groups using multiple features. However, existing multi-view clustering methods fail to capturing the grouping effect and local geometrical structure of the multiple features. In order to solve these problems, this paper proposes a novel multi-view subspace clustering model called graph-regularized least squares regression (GLSR), which uses not only the least squares regression instead of the nuclear norm to generate grouping effect, but also the manifold constraint to preserve the local geometrical structure of multiple features. Specifically, the proposed GLSR method adopts the least squares regression to learn the globally consensus information shared by multiple views and the column-sparsity norm to measure the residual information. Under the alternating direction method of multipliers framework, an effective method is developed by iteratively update all variables. Numerical studies on eight real databases demonstrate the effectiveness and superior performance of the proposed GLSR over eleven state-of-the-art methods.
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