基于t-SVD的结构保持多视图子空间聚类

doi:10.11896/jsjkx.210800215

摘要/Abstract

摘要： 针对基于张量的多视图子空间聚类算法不能很好地保持样本之间的流形几何结构和多视图之间相似性的缺点,提出了一种结构保持的t-SVD多视图子空间聚类算法。首先将重构系数作为数据构造描述流形结构的邻接矩阵,其次通过图正则限制多视图数据的重构系数,然后利用各个视图的重构系数计算描述视图之间关系的相似矩阵,最后通过交替优化的方式来分别优化邻接矩阵及相似矩阵和多视图数据的重构系数,直至收敛。在3个数据库上分别进行了聚类实验,准确率分别达到了97.25%,96.97%,100%。实验结果表明,所提算法在聚类任务上具有较高的准确率。

关键词: 子空间聚类, 多视图学习, 结构保持, 张量, t-SVD

Abstract: To peruse the manifold structure and correlation among multi-view data for the tensor based subspace clustering algorithms,this paper proposes a novel algorithm named structure preserved multi-view subspace clustering based on t-SVD(t-SVD-SpMSC).For both structures in multi-view data,we employ the graph regularization in which the graph is got adaptively by iteration.To optimize the objective function,we develop an alternative optimization algorithm to solve the final objective function.The accuracy of clustering using t-SVD-SpMSC on three datasets is 100%,91.51%,99.81% respectively,which shows the priority of the proposed method.

Key words: Subspace clustering, Multi-view learning, Structure preserved, Tensor, t-SVD

中图分类号:

TP394.1

张华伟, 陆新东, 朱小明, 孙军涛. 基于t-SVD的结构保持多视图子空间聚类[J]. 计算机科学, 2022, 49(11A): 210800215-6. https://doi.org/10.11896/jsjkx.210800215

ZHANG Hua-wei, LU Xin-dong, ZHU Xiao-ming, SUN Jun-tao. Structure Preserved Multi-view Subspace Clustering Based on t-SVD[J]. Computer Science, 2022, 49(11A): 210800215-6. https://doi.org/10.11896/jsjkx.210800215

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