苏志勋

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:数学科学学院

学科:计算数学

办公地点:创新园大厦(海山楼)B1313

联系方式:84708351-8093

电子邮箱:zxsu@dlut.edu.cn

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Robust subspace learning-based low-rank representation for manifold clustering

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论文类型:期刊论文

第一作者:Tang, Kewei

通讯作者:Zhang, J (reprint author), Liaoning Normal Univ, Sch Math, Huanghe Rd, Dalian 116029, Peoples R China.

合写作者:Su, Zhixun,Jiang, Wei,Zhang, Jie,Sun, Xiyan,Luo, Xiaonan

发表时间:2019-11-01

发表刊物:NEURAL COMPUTING & APPLICATIONS

收录刊物:SCIE

卷号:31

期号:11

页面范围:7921-7933

ISSN号:0941-0643

关键字:Subspace learning; Low-rank representation; Manifold clustering; Spectral clustering-based methods

摘要:Spectral clustering-based subspace clustering methods have attracted broad interest in recent years. This kind of methods usually uses the self-representation in the original space to extract the affinity between the data points. However, we can usually find a subspace where the affinity of the projected data points can be extracted by self-representation more effectively. Moreover, only using the self-representation in the original space cannot handle nonlinear manifold clustering well. In this paper, we present robust subspace learning-based low-rank representation learning a subspace favoring the affinity extraction for the low-rank representation. The process of learning the subspace and yielding the representation is conducted simultaneously, and thus, they can benefit from each other. After extending the linear projection to nonlinear mapping, our method can handle manifold clustering problem which can be viewed as a general case of subspace clustering. In addition, the l2,1-norm used in our model can increase the robustness of our method. Extensive experimental results demonstrate the effectiveness of our method on manifold clustering.