CASIA OpenIR  > 多模态人工智能系统全国重点实验室
Adaptive-weighted deep multi-view clustering with uniform scale representation
Chen, Rui1,2; Tang, Yongqiang2; Zhang, Wensheng1,2; Feng, Wenlong1,3
Source PublicationNEURAL NETWORKS
ISSN0893-6080
2024-03-01
Volume171Pages:114-126
Corresponding AuthorTang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
AbstractMulti-view clustering has attracted growing attention owing to its powerful capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally fail to distinguish the unequal importance of multiple views to the clustering task and overlook the scale uniformity of learned latent representation among different views, resulting in blurry physical meaning and suboptimal model performance. To address these issues, in this paper, we propose a joint learning framework, termed Adaptive-weighted deep Multi-view Clustering with Uniform scale representation (AMCU). Specifically, to achieve more reasonable multi-view fusion, we introduce an adaptive weighting strategy, which imposes simplex constraints on heterogeneous views for measuring their varying degrees of contribution to consensus prediction. Such a simple yet effective strategy shows its clear physical meaning for the multi view clustering task. Furthermore, a novel regularizer is incorporated to learn multiple latent representations sharing approximately the same scale, so that the objective for calculating clustering loss cannot be sensitive to the views and thus the entire model training process can be guaranteed to be more stable as well. Through comprehensive experiments on eight popular real-world datasets, we demonstrate that our proposal performs better than several state-of-the-art single-view and multi-view competitors.
KeywordMulti-view clustering Deep clustering Adaptive-weighted learning Uniform scale representation
DOI10.1016/j.neunet.2023.11.066
WOS KeywordNONNEGATIVE MATRIX FACTORIZATION
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Develop-ment Program of China[2020AAA0109500] ; National Natural Science Foundation of China[62106266] ; National Natural Science Foundation of China[U22B2048]
Funding OrganizationNational Key Research and Develop-ment Program of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:001139926700001
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/54796
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorTang, Yongqiang; Zhang, Wensheng
Affiliation1.Hainan Univ, Coll Informat Sci & Technol, Haikou 570208, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
3.Hainan Univ, State Key Lab Marine Resource Utilizat South China, Haikou 570208, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen, Rui,Tang, Yongqiang,Zhang, Wensheng,et al. Adaptive-weighted deep multi-view clustering with uniform scale representation[J]. NEURAL NETWORKS,2024,171:114-126.
APA Chen, Rui,Tang, Yongqiang,Zhang, Wensheng,&Feng, Wenlong.(2024).Adaptive-weighted deep multi-view clustering with uniform scale representation.NEURAL NETWORKS,171,114-126.
MLA Chen, Rui,et al."Adaptive-weighted deep multi-view clustering with uniform scale representation".NEURAL NETWORKS 171(2024):114-126.
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