Deep Consistent-Inherent Learning for Cross-Modal Subspace Clustering  

作　　者：Yuzhuo Feng Demin Zhou 

机构地区：[1]Xidian University Xi'an,710071,P.R.China [2]The Tenth Research Institute of China Electronics Technology Group Corporation Chengdu,610036,P.R.China

出　　处：《Guidance, Navigation and Control》2024年第3期129-146,共18页制导、导航与控制(英文)

摘　　要：Deep cross-modal clustering has been developing at a rapid pace and attracted great attention.It aims to pursue a consistent subspace from different modalities by conventional neural network and achieve remarkable clustering performance. However, most existing deep cross-modal clustering methods do not simultaneously take care of the inherently different information for each modality and local geometric structure for all cross-modal data, which inevitably results in the degradation of clustering performance. In this paper, we propose a novel method named Deep Consistent-Inherent Cross-Modal Subspace Clustering(i.e. DCCSC) to tackle these problems of cross-modal clustering. Our method can preserve the inherent independence of each modality while exploring the consistent information amongst different modalities. Meanwhile, a neighbor graph is embedded into the proposed deep cross-modal subspace clustering framework to maintain the local geometry structure of the original data and learn a shared subspace representation. Therefore, we integrate the consistent-inherent learning and the local structure learning into a unified deep framework to significantly improve the cross-modal subspace clustering performance. Experimental results demonstrate that our proposed method can achieve the superior clustering performance compared with the state-of-the-art methods on four benchmark datasets.

关 键 词：Multi-view clustering consistent-inherent learning local structure 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术]