[1]王丽娟,丁世飞,夏菁.基于多样性的多视图低秩稀疏子空间聚类算法[J].智能系统学报,2023,18(2):399-408.[doi:10.11992/tis.202110026]
WANG Lijuan,DING Shifei,XIA Jing.Multiview low-rank sparse subspace clustering based on diversity[J].CAAI Transactions on Intelligent Systems,2023,18(2):399-408.[doi:10.11992/tis.202110026]
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WANG Lijuan,DING Shifei,XIA Jing.Multiview low-rank sparse subspace clustering based on diversity[J].CAAI Transactions on Intelligent Systems,2023,18(2):399-408.[doi:10.11992/tis.202110026]
基于多样性的多视图低秩稀疏子空间聚类算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第2期
页码:
399-408
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2023-05-05
- Title:
- Multiview low-rank sparse subspace clustering based on diversity
- Keywords:
- multiview clustering; subspace representation; diversity representation; low-rank sparse constraint; spectral clustering; machine learning; feature learning; data mining
- 分类号:
- TP391
- 摘要:
- 本文主要研究如何通过挖掘多视图特征的多样性信息来促进多视图聚类，提出了基于多样性的多视图低秩稀疏子空间聚类算法。该方法直接将视图多样性概念应用于多视图低秩稀疏子空间聚类算法框架中，确保不同视图的子空间表示矩阵的多样性；为了实现多个视图聚类一致性同时达到提高聚类性能的目标，在该框架中引入谱聚类算法共同优化求解。通过对3个图像数据集的实验验证了该算法的有效性，同时其聚类的性能优于已有的单视图及多视图算法。
- Abstract:
- This paper focuses on boosting multiview clustering by exploring the diversity of information among multiview features. A multiview clustering framework, called multiview low-rank sparse subspace clustering based on diversity, is proposed for this task. In the proposed method, the concept of diversity is successfully introduced into the framework of a multiview low-rank sparse subspace clustering algorithm to ensure that the representation matrix of different subspace views has certain differences and that the obtained information is diversified. In addition, the spectral clustering algorithm is added to the framework to achieve a joint optimization solution, which can markedly improve the clustering performance, to obtain a unified target clustering assignment. The effectiveness of the algorithm is verified by fully observing three image datasets, and the clustering performance of the proposed algorithm is better than that of the existing single-view and multiview algorithms.
- 参考文献/References:
-
[1] 夏菁. 多视角子空间聚类算法研究[D]. 徐州: 中国矿业大学, 2021: 3?24.
XIA Jing. Research on multi-view subspace clustering algorithms[D]. Xuzhou: China University of Mining and Technology, 2021: 3?24.
[2] YANG Yan, WANG Hao. Multi-view clustering: a survey[J]. Big data mining and analytics, 2018, 1(2): 83–107.
[3] 刘相男, 丁世飞, 王丽娟. 基于深度图正则化矩阵分解的多视图聚类算法[J]. 智能系统学报, 2022, 17(1): 158–169
LIU Xiangnan, DING Shifei, WANG Lijuan. A multi-view clustering algorithm based on deep matrix factorization with graph regularization[J]. CAAI transactions on intelligent systems, 2022, 17(1): 158–169
[4] WEI Lai, WANG Xiaofeng, WU Aihua, et al. Robust subspace segmentation by self-representation constrained low-rank representation[J]. Neural processing letters, 2018, 48(3): 1671–1691.
[5] 夏菁, 丁世飞. 基于低秩稀疏约束的自权重多视角子空间聚类[J]. 南京大学学报(自然科学版), 2020, 56(6): 862–869
XIA Jing, DING Shifei. Self-weighted multi-view subspace clustering with low-rank sparse constrain[J]. Journal of Nanjing university (natural science edition), 2020, 56(6): 862–869
[6] XUE Zhe, DU Junping, DU Dawei, et al. Deep low-rank subspace ensemble for multi-view clustering[J]. Information sciences, 2019, 482: 210–227.
[7] 董文华. 稀疏子空间聚类及应用研究[D]. 无锡: 江南大学, 2019: 11?15.
DONG Wenhua. Research of sparse subspace clustering and its applications[D]. Wuxi: Jiangnan University, 2019: 11?15.
[8] 张小乾. 子空间聚类模型与算法研究[J]. 南京:南京理工大学, 2021: 6–10
Zhang Xiaoqian. Research on Subspace Clustering Model and Algorithm[J]. Nanjing: Nanjing University of Science & Technology, 2021: 6–10
[9] WANG Yuxiang, XU Huan, LENG Chenlei. Provable subspace clustering: when LRR meets SSC[J]. IEEE transactions on information theory, 2014, 5(7): 5406?5432.
[10] LI Chunguang, YOU Chong, VIDAL R. Structured sparse subspace clustering: a joint affinity learning and subspace clustering framework[J]. IEEE transactions on image processing, 2017, 26(6): 2988–3001.
[11] BRBI? M, KOPRIVA I. Multi-view low-rank sparse subspace clustering[J]. Pattern recognition, 2018, 73: 247–258.
[12] KUMAR A, RAI P, DAUME H, et al. Co-regularized multi-view spectral clustering[C]// Proceedings of the 24th International Conference on Neural Information Processing Systems. Red Hook: Currant Associates Inc., 2011: 1413?1421.
[13] YIN Qiyue, WU Shu, HE Ran, et al. Multi-view clustering via pairwise sparse subspace representation[J]. Neurocomputing, 2015, 156: 12–21.
[14] KANG Zhao, SHI Guoxin, HUANG Shudong, et al. Multi-graph fusion for multi-view spectral clustering[J]. Knowledge-based systems, 2020, 189: 105102.
[15] ZHANG Changqing, HU Qinghua, FU Huazhu, et al. Latent multi-view subspace clustering[C]//IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4333?4341.
[16] CAO Xiaochun, ZHANG Changqing, FU Huazhu, et al. Diversity-induced multi-view subspace clustering[C]//IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 586?594.
[17] WANG Xiaobo, GUO Xiaojie, LEI Zhen, et al. Exclusivity-consistency regularized multi-view subspace clustering[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 1?9.
[18] 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.
[19] CAI Jianfeng, CANDèS E J, SHEN Zuowei. A singular value thresholding algorithm for matrix completion[J]. SIAM journal on optimization, 2010, 20(4): 1956–1982.
[20] DONOHO D L. De-noising by soft-thresholding[J]. IEEE transactions on information theory, 1995, 41(3): 613–627.
[21] DAUBECHIES I, DEFRISE M, DE MOL C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on pure and applied mathematics, 2004, 57(11): 1413–1457.
[22] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(7): 971–987.
[23] WISKOTT L, KRüGER N, KUIGER N, et al. Face recognition by elastic bunch graph matching[J]. IEEE transactions on pattern analysis and machine intelligence, 1997, 19(7): 775–779.
[24] LADES M, VORBRUGGEN J C, BUHMANN J, et al. Distortion invariant object recognition in the dynamic link architecture[J]. IEEE transactions on computers, 1993, 42(3): 300–311.
[25] ZHANG Changqing, FU Huazhu, LIU Si, et al. Low-rank tensor constrained multiview subspace clustering[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1582?1590.
[26] NG A Y, JORDAN M I, WEISS Y. On spectral clustering: Analysis and an algorithm[C]//Advances in Neural Information Processing Systems. Boston: MIT Press, 2002: 849-856
[27] NIE Feiping, CAI Guohao, LI Xuelong. Multi-view clustering and semi-supervised classification with adaptive neighbours[C]//Proceedings of the 31th AAAI Conference on Artificial Intelligence, New York: ACM, 2017: 2408-2414.
[28] NIE Feiping, LI Jing, LI Xuelong. Parameter-free auto-weighted multiple graph learning: A framework for multi-view clustering and semi-supervised classification[C]// Proceedings of 25th International Joint Conference on Artificial Intelligence. New York: AAAI Press, 2016: 1881?1887.
- 相似文献/References:
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[1]刘相男,丁世飞,王丽娟.基于深度图正则化矩阵分解的多视图聚类算法[J].智能系统学报,2022,17(1):158.[doi:10.11992/tis.202104046]
LIU Xiangnan,DING Shifei,WANG Lijuan.A multi-view clustering algorithm based on deep matrix factorization with graph regularization[J].CAAI Transactions on Intelligent Systems,2022,17():158.[doi:10.11992/tis.202104046]
[2]马睿,周治平.结合地标点与自编码的快速多视图聚类网络[J].智能系统学报,2022,17(2):333.[doi:10.11992/tis.202101011]
MA Rui,ZHOU Zhiping.Fast multiview clustering network combining landmark points and autoencoder[J].CAAI Transactions on Intelligent Systems,2022,17():333.[doi:10.11992/tis.202101011]
备注/Memo
收稿日期:2021-10-23。
基金项目:国家自然科学基金项目（61976216, 62276265, 61672522）；江苏省高等教育改革研究课题（2021JSJG488）；江苏省高等职业院校专业带头人高端研修项目（2022GRGDYX087）.
作者简介:王丽娟,副教授,博士,CCF会员,江苏省青蓝工程骨干教师,主要研究方向为机器学习、聚类分析。参加国家自然科学基金面上项目2项,主持省级教科研课题2项,发表学术论文12篇;丁世飞,教授,博士生导师,博士,CCF杰出会员,第八届吴文俊人工智能科学技术奖获得者,主要研究方向为人工智能与模式识别、机器学习与数据挖掘。主持国家重点基础研究计划课题1项、主持国家自然科学基金面上项目4项。出版专著5部,发表学术论文200余篇;夏菁,硕士研究生,主要研究方向为机器学习、聚类分析
通讯作者:丁世飞. E-mail：dingsf@cumt.edu.cn
更新日期/Last Update:
1900-01-01