面向视角非对齐数据的多视角聚类方法

doi:10.11959/j.issn.1000-436x.2022134

Journal on Communications ›› 2022, Vol. 43 ›› Issue (7): 143-152.doi: 10.11959/j.issn.1000-436x.2022134

• Papers • Previous Articles Next Articles

Multiview clustering method for view-unaligned data

Ao LI¹, Cong FENG¹, Yutong NIU¹, Shibiao XU², Yingtao ZHANG³, Guanglu SUN¹

¹ School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
² Artificial Intelligence School, Beijing University of Posts and Telecommunications, Beijing 100876, China
³ School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Revised:2022-06-21 Online:2022-07-25 Published:2022-06-01
Supported by:
The National Natural Science Foundation of China(62071157);The University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT-2018203);Heilongjiang Natural Science Foundation for Excellent Young Scholars(YQ2019F011);The Fundamental Research Foundation for University of Heilongjiang Province(LGYC2018JQ013);Postdoctoral Foundation of Heilongjiang Province(LBH-Q19112)

Abstract

Abstract:

A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was employed based on multi-view non-negative matrix factorization to embed original features into a measurable low-dimensional subspace.Second, view-alignment relationships were modeled as optimal matching of a bipartite graph, which could be generalized to multiple-views situations via the proposed concept reference view.Representation learning and data alignment were further integrated into a unified Bi-level optimization framework to mutually boost the two learning processes, effectively enhancing the ability to learn from view-unaligned data.Extensive experimental results of view-unaligned clustering on three public datasets demonstrate that the proposed method outperforms eight advanced multiview clustering methods on multiple evaluation metrics.

Key words: clustering analysis, multiview learning, view-unaligned data, non-negative matrix factorization

CLC Number:

TP391

Ao LI, Cong FENG, Yutong NIU, Shibiao XU, Yingtao ZHANG, Guanglu SUN. Multiview clustering method for view-unaligned data[J]. Journal on Communications, 2022, 43(7): 143-152.

Figures/Tables 7

References 22

[1]	ZHAO J , XIE X J , XU X ,et al. Multi-view learning overview:recent progress and new challenges[J]. Information Fusion, 2017,38: 43-54.
[2]	CHAO G Q , SUN S L , BI J B . A survey on multi-view clustering[J]. IEEE Transactions on Artificial Intelligence, 2021,2(2): 146-168.
[3]	KANG Z , LIN Z , ZHU X ,et al. Structured graph learning for scalable subspace clustering:from single view to multiview[J]. IEEE Transactions on Cybernetics,2021:doi.org/ 10.1109/TCYB.2021.3061660.
[4]	HUANG A P , CHEN W L , ZHAO T S ,et al. Joint learning of latent similarity and local embedding for multi-view clustering[J]. IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society, 2021,30: 6772-6784.
[5]	HUANG Z M , REN Y Z , PU X R ,et al. Dual self-paced multi-view clustering[J]. Neural Networks, 2021,140: 184-192.
[6]	TAO Z Q , LIU H F , LI S ,et al. Marginalized multiview ensemble clustering[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020,31(2): 600-611.
[7]	GAO Q X , XIA W , WAN Z Z ,et al. Tensor-SVD based graph learning for multi-view subspace clustering[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2020: 3930-3937.
[8]	李骜, 王卓, 于晓洋 ,等. 多核低冗余表示学习的稳健多视图子空间聚类方法[J]. 通信学报, 2021,42(11): 193-204.
	LI A , WANG Z , YU X Y ,et al. Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning[J]. Journal on Communications, 2021,42(11): 193-204.
[9]	NIE F , LI J , LI X . Parameter-free auto-weighted multiple graph learning:a framework for multiview clustering and semi-supervised classification[C]// Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.[S.l.:s.n.], 2016: 1881-1887.
[10]	NIE F P , LI J , LI X L . Self-weighted multiview clustering with multiple graphs[C]// Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence.[S.l.:s.n.], 2017: 2564-2570.
[11]	LIANG Y W , HUANG D , WANG C D . Consistency meets inconsistency:a unified graph learning framework for multi-view clustering[C]// Proceedings of 2019 IEEE International Conference on Data Mining. Piscataway:IEEE Press, 2019: 1204-1209.
[12]	WANG H , YANG Y , LIU B . GMC:graph-based multi-view clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2020,32(6): 1116-1129.
[13]	LIU J L , WANG C , GAO J ,et al. Multi-view clustering via joint nonnegative matrix factorization[C]// Proceedings of the 2013 SIAM International Conference on Data Mining. Philadelphia:Society for Industrial and Applied Mathematics, 2013: 252-260.
[14]	张祎, 孔祥维, 王振帆 ,等. 基于多视图矩阵分解的聚类分析[J]. 自动化学报, 2018,44(12): 2160-2169.
	ZHANG Y , KONG X W , WANG Z F ,et al. Matrix factorization for multi-view clustering[J]. Acta Automatica Sinica, 2018,44(12): 2160-2169.
[15]	ZHANG C Q , FU H Z , LIU S ,et al. Low-rank tensor constrained multiview subspace clustering[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015: 1582-1590.
[16]	WU J L , LIN Z C , ZHA H B . Essential tensor learning for multi-view spectral clustering[J]. IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society, 2019,28(12): 5910-5922.
[17]	HUANG Z Y , HU P , ZHOU J T ,et al. Partially view-aligned clustering[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems.New York:Curran Associates Inc. , 2020: 2892-2902.
[18]	YANG M X , LI Y F , HUANG Z Y ,et al. Partially view-aligned representation learning with noise-robust contrastive loss[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2021: 1134-1143.
[19]	CUI H , ZHANG J J , CUI C F ,et al. Solving large-scale assignment problems by Kuhn-Munkres algorithm[C]// Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016). Paris:Atlantis Press, 2016: 822-827.
[20]	COLSON B , MARCOTTE P , SAVARD G . An overview of bilevel optimization[J]. Annals of Operations Research, 2007,153(1): 235-256.
[21]	KANG P P , LIN Z H , YANG Z G ,et al. Intra-class low-rank regularization for supervised and semi-supervised cross-modal retrieval[J]. Applied Intelligence, 2022,52(1): 33-54.
[22]	ZHANG D L , WU X J . Robust and discrete matrix factorization hashing for cross-modal retrieval[J]. Pattern Recognition, 2022,122:108343.

Metrics

Recommended 0

No Suggested Reading articles found!

Multiview clustering method for view-unaligned data

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 22

Related Articles 4

Metrics

Recommended 0

[1]	Ao LI, Zhuo WANG, Xiaoyang YU, Deyun CHEN, Yingtao ZHANG, Guanglu SUN. Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning [J]. Journal on Communications, 2021, 42(11): 193-204.
[2]	Rong-hua CHI,Yuan CHENG,Su-xia ZHU,Shao-bin HUANG,De-yun CHEN. Uncertain data analysis algorithm based on fast Gaussian transform [J]. Journal on Communications, 2017, 38(3): 101-111.
[3]	Miao YU,Wu YANG,Wei WANG,wei SHENGuo. Co-clustering of multi-entities sparse relational data in microblogging [J]. Journal on Communications, 2016, 37(1): 151-159.
[4]	Xiang-lin WEI,Ming CHEN,Guo-min ZHANG,Jian-jun HUANG. NMF-NAD:detecting network-wide traffic anomaly based on NMF [J]. Journal on Communications, 2012, 33(4): 54-61.