聚类分析
计算机科学
加权
判别式
人工智能
互补性(分子生物学)
子空间拓扑
模式识别(心理学)
约束(计算机辅助设计)
代表(政治)
特征学习
数据挖掘
比例(比率)
相似性(几何)
数学
医学
物理
量子力学
放射科
遗传学
几何学
生物
政治
政治学
法学
图像(数学)
作者
Jiao Wang,Bin Wu,Zhenwen Ren,Hongying Zhang,Yunhui Zhou
标识
DOI:10.1016/j.eswa.2022.119031
摘要
Deep subspace clustering methods for multi-view have achieved impressive clustering performance over other clustering methods. However, the existing methods either cannot integrate the global and local information of multi-view or fail to explore the discriminative contributions among views. In this paper, we propose a novel multi-scale deep multi-view subspace clustering (MDMVSC) method, which unifies the multi-scale learning (ML) module, self-weighting fusion (SF) module and structure preserving (SP) constraint. Specifically, to take advantage of the complementarity and diversity of different views, ML module first learns specific self-representation matrix for each view from the multi-scale low-dimensional latent features with the global and local information. Then, using the SF module, these matrices are fused to obtain the consensus representation of multi-view via attention mechanism guided weights according to their discriminative contributions. Moreover, SP constraint encourages the multi-scale latent features to preserve the consistent structural information of the original multi-view for enhancing representation ability. Extensive experimental results on five datasets demonstrate the superiority of MDMVSC in comparison with several state-of-the-art methods.
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