您好，欢迎访问山东省农业科学院文献资源数据库平台

Kernel-based low-rank tensorized multiview spectral clustering

文献类型： 外文期刊

作者： Yu, Xiao 1 ; Liu, Hui 1 ; Wu, Yan 3 ; Ruan, Huaijun 4 ;

作者机构： 1.Shandong Univ Finance & Econ, Dept Comp Sci & Technol, Jinan, Peoples R China

2.Shandong Key Lab Digital Media Technol, Jinan, Shandong, Peoples R China

3.Stanford Univ, Med Ctr, Palo Alto, CA 94304 USA

4.Shandong Acad Agr Sci, S&T Informat Inst, Jinan, Shandong, Peoples R China

关键词： kernel; low‐ rank; multiview clustering; spectral clustering; tensor

期刊名称：INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS （ 影响因子：8.709； 五年影响因子：8.161 ）

ISSN： 0884-8173

年卷期：

页码：

收录情况： SCI

摘要： Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high-dimensional data, whose noise goes against the clustering results. Besides, some methods based on self-representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel-based Low-rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high-dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low-rank tensor is learned in the process of exploring the high-order correlations between data. Experiments on real-world data sets show that our method not only yields better results but also is quite time-saving compared with other state-of-the-art models.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：山东省农业科学院文献资源数据库平台

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台