多核低冗余表示学习的稳健多视图子空间聚类方法

doi:10.11959/j.issn.1000-436x.2021217

Journal on Communications ›› 2021, Vol. 42 ›› Issue (11): 193-204.doi: 10.11959/j.issn.1000-436x.2021217

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Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning

Ao LI¹^,², Zhuo WANG¹, Xiaoyang YU², Deyun CHEN¹, Yingtao ZHANG³, Guanglu SUN¹

¹ School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
² Postdoctoral Station of Instrument Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
³ School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Revised:2021-11-02 Online:2021-11-25 Published:2021-11-01
Supported by:
The National Natural Science Foundation of China(62071157);The Natural Science Foundation of Heilongjiang Province(YQ2019F011);University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT-2018203);The Fundamental Research Founds for the University of Heilongjiang Province(LGYC2018JQ013);Postdoctoral Foundation of Heilongjiang Province(LBH-Q19112)

Abstract

Abstract:

Considering the impact of high dimensional data redundancy and noise interference on multiview subspace clustering, a robust multiview subspace clustering method based on multi-kernel low redundancy representation learning was proposed.Firstly, by analyzing and revealing the redundancy and noise influence characteristics of data in kernel space, a multi-kernel learning method was proposed to obtain a robust low-redundancy representation of local view-specific data, which was utilized to replace the original data to implement subspace learning.Secondly, a tensor analysis model was introduced to carry out multiview fusion, so as to learn the potential low-rank tensor structure among different subspace representations from global perspective.It would capture the high-order correlation among views while maintaining their unique information.In this method, robust low-redundancy representation learning, view-specific subspace learning and fusion potential subspace structure learning were unified into the same objective function, so that they could promote each other during iterations.A large number of experimental results demonstrate that the proposed method is superior to the existing mainstream multiview clustering methods on several objective evaluation indicators.

Key words: low-redundancy representation learning, subspace clustering, multiview learning, tensor analysis

CLC Number:

TP391

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.

Figures/Tables 8

方法	ACC	NMI	F-measure
LMSC	0.915±0.00	0.839±0.00	0.900±0.00
MSSC	0.971±0.00	0.898±0.00	0.944±0.00
PMSC	0.472±0.04	0.202±0.08	0.445±0.04
FMR	0.946±0.01	0.832±0.02	0.898±0.02
CoMSC	0.963±0.00	0.881±0.00	0.923±0.00
本文方法	$0 . 980 \pm 0 . 00$	$0 . 946 \pm 0 . 00$	$0 . 973 \pm 0 . 00$

方法	ACC	NMI	F-measure
LMSC	0.811±0.03	0.908±0.02	0.747±0.04
MSSC	0.832±0.02	$0 . 927 \pm 0 . 01$	0.787±0.02
PMSC	0.551±0.04	0.732±0.03	0.394±0.06
FMR	0.809±0.02	0.898±0.01	0.741±0.02
CoMSC	0.812±0.01	0.901±0.00	0.749±0.01
本文方法	$0 . 846 \pm 0 . 03$	0.923±0.01	$0 . 791 \pm 0 . 02$

方法	ACC	NMI	F-measure
LMSC	0.763±0.05	0.718±0.02	0.682±0.04
MSSC	0.924±0.00	0.859±0.00	0.858±0.00
PMSC	0.636±0.07	0.624±0.04	0.576±0.04
FMR	0.865±0.01	0.771±0.01	0.760±0.01
CoMSC	0.930±0.00	0.870±0.00	0.866±0.00
本文方法	$0 . 974 \pm 0 . 00$	$0 . 943 \pm 0 . 00$	$0 . 949 \pm 0 . 00$

References 20

[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]	ZHANG C Q , HU Q H , FU H Z ,et al. Latent multi-view subspace clustering[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2017: 4333-4341.
[3]	ELHAMIFAR E , VIDAL R . Sparse subspace clustering:algorithm,theory,and applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(11): 2765-2781.
[4]	LIU G C , LIN Z C , YAN S C ,et al. Robust recovery of subspace structures by low-rank representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(1): 171-184.
[5]	FU Y F , GAO J B , TIEN D ,et al. Tensor LRR and sparse coding-based subspace clustering[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016,27(10): 2120-2133.
[6]	WANG J , WANG X , TIAN F ,et al. Constrained low-rank representation for robust subspace clustering[J]. IEEE Transactions on Cybernetics, 2017,47(12): 4534-4546.
[7]	YANG J F , LIANG J , WANG K ,et al. Subspace clustering via good neighbors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020,42(6): 1537-1544.
[8]	XU C , TAO D , XU C . A survey on multi-view learning[J]. arXiv Preprint,arXiv:1304.5634, 2013.
[9]	CHAUDHURI K , KAKADE S M , LIVESCU K ,et al. Multi-view clustering via canonical correlation analysis[C]// Proceedings of Proceedings of the 26th Annual International Conference on Machine Learning. New York:ACM Press, 2009: 129-136.
[10]	GAO H C , NIE F P , LI X L ,et al. Multi-view subspace clustering[C]// Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2015: 4238-4246.
[11]	LI R H , ZHANG C Q , HU Q H ,et al. Flexible multi-view representation learning for subspace clustering[C]// Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.[S.n.:s.l], 2019: 2916-2922.
[12]	张茁涵, 曹容玮, 李晨 ,等. 隐式低秩稀疏表示的多视角子空间聚类[J]. 模式识别与人工智能, 2020,33(4): 344-352.
	ZHANG Z H , CAO R W , LI C ,et al. Latent low-rank sparse multi-view subspace clustering[J]. Pattern Recognition and Artificial Intelligence, 2020,33(4): 344-352.
[13]	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 (ICCV). Piscataway:IEEE Press, 2015: 1582-1590.
[14]	ABAVISANI M , PATEL V M . Multimodal sparse and low-rank subspace clustering[J]. Information Fusion, 2018,39: 168-177.
[15]	KANG Z , ZHAO X J , PENG C ,et al. Partition level multiview subspace clustering[J]. Neural Networks, 2020,122: 279-288.
[16]	LIU J Y , LIU X W , YANG Y X ,et al. Multiview subspace clustering via co-training robust data representation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021,PP(99): 1-13.
[17]	CAO X C , ZHANG C Q , FU H Z ,et al. Diversity-induced Multi-view subspace clustering[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2015: 586-594.
[18]	SCH?LKOPF B , SMOLA A , MüLLER K R , . Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998,10(5): 1299-1319.
[19]	BOYD S . Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends? in Machine Learning, 2010,3(1): 1-122.
[20]	KILMER M E , BRAMAN K , HAO N ,et al. Third-order tensors as operators on matrices:a theoretical and computational framework with applications in imaging[J]. SIAM Journal on Matrix Analysis and Applications, 2013,34(1): 148-172.

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Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning

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