Robust multi-view clustering algorithm based on adaptive neighborhood

doi:10.11772/j.issn.1001-9081.2020060828

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (4): 1093-1099.DOI: 10.11772/j.issn.1001-9081.2020060828

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Robust multi-view clustering algorithm based on adaptive neighborhood

LI Xingfeng¹, HUANG Yuqing¹, REN Zhenwen², LI Yihong²

1. School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
2. School of National Defense Science and Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China

Received:2020-06-16 Revised:2020-10-28 Online:2021-04-10 Published:2020-12-22
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61673220), the Sichuan Major Science and Technology Project (2018TZDZX0002).

基于自适应邻域的鲁棒多视图聚类算法

李杏峰¹, 黄玉清¹, 任珍文², 李毅红²

1. 西南科技大学信息工程学院, 四川绵阳 621010;
2. 西南科技大学国防科技学院, 四川绵阳 621010

通讯作者: 黄玉清
作者简介:李杏峰（1993—），男，四川自贡人，硕士研究生，CCF会员，主要研究方向：多视图聚类、多核聚类；黄玉清（1962—），女，四川绵阳人，教授，博士，主要研究方向：信号特征提取与识别、图像处理、压缩感知；任珍文（1987—），男，四川绵阳人，讲师，博士，CCF会员，主要研究方向：多核聚类、多视图聚类、非负矩阵分解、压缩感知；李毅红（1998—），男，四川平昌人，主要研究方向：多视图聚类。
基金资助:
国家自然科学基金资助项目（61673220）；四川省重大科技项目（2018TZDZX0002）。

Abstract

Abstract: Since the existing adaptive neighborhood based multi-view clustering algorithms do not consider the noise and the loss of consensus graph information, a Robust Multi-View Graph Clustering(RMVGC) algorithm based on adaptive neighborhood was proposed. Firstly, to avoid the influence of noise and outliers on the data, the Robust Principal Component Analysis(RPCA) model was used to learn multiple clean low-rank data from the original data. Secondly, the adaptive neighborhood learning was employed to directly fuse multiple clean low-rank data to obtain a clean consensus affinity graph, thus reducing the information loss in the process of graph fusion. Experimental results demonstrate that the Normalized Mutual Informations(NMI) of the proposed algorithm RMVGC is improved by 5.2, 1.36, 27.2, 4.66 and 5.85 percentage points, respectively, compared to the current popular multi-view clustering algorithms on MRSCV1, BBCSport, COIL20, ORL and UCI digits datasets. Meanwhile, in the proposed algorithm, the local structure of data is maintained, the robustness against the original data is enhanced, the quality of affinity graph is improved, and such that the proposed algorithm has great clustering performance on multi-view datasets.

Key words: adaptive neighborhood, multi-view clustering, low-rank, robustness, consensus affinity graph learning

摘要： 针对现存的基于自适应邻域的多视图聚类算法没有考虑噪声和共识图信息损失的问题，提出一种基于自适应邻域的鲁棒多视图聚类（RMVGC）算法。首先，为了避免噪声和异常值对数据的影响，通过鲁棒主成分分析模型（RPCA）从原始数据中学习多个干净的低秩数据；其次，用自适应邻域学习直接融合多个干净的低秩数据来得到一个干净的共识关系图，从而减少图融合过程中的信息丢失。实验结果表明，所提RMVGC算法的标准化互信息（NMI）在MRSCV1、BBCSport、COIL20、ORL和UCI digits数据集上比目前流行的多视图聚类算法分别提升了5.2、1.36、27.2、4.66和5.85个百分点。同时，该算法保持了数据局部结构，增强了对原始数据的鲁棒性，提高了关系图质量，在多视图数据集上具有较好的聚类性能。

关键词: 自适应邻域, 多视图聚类, 低秩, 鲁棒, 共识关系图学习

CLC Number:

TP301.6

LI Xingfeng, HUANG Yuqing, REN Zhenwen, LI Yihong. Robust multi-view clustering algorithm based on adaptive neighborhood[J]. Journal of Computer Applications, 2021, 41(4): 1093-1099.

李杏峰, 黄玉清, 任珍文, 李毅红. 基于自适应邻域的鲁棒多视图聚类算法[J]. 计算机应用, 2021, 41(4): 1093-1099.

References

[1] NIE F,SHI S,LI X. Auto-weighted multi-view co-clustering via fast matrix factorization[J]. Pattern Recognition, 2020, 102:No. 107207.
[2] TANG C,ZHU X,LIU X,et al. Learning a joint affinity graph for multiview subspace clustering[J]. IEEE Transactions on Multimedia,2019,21(7):1724-1736.
[3] ZHANG C,HU Q,FU H,et al. Latent multi-view subspace clustering[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2017:4333-4341.
[4] LIU X,WANG L,ZHU X,et al. Absent multiple kernel learning algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(6):1303-1316.
[5] ZHANG C,HAN Z,CUI Y,et al. CPM-Nets:cross partial multiview networks[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook:Curran Associates Inc.,2019:557-567.
[6] WANG H,YANG Y,LIU B,et al. A study of graph-based system for multi-view clustering[J]. Knowledge-Based Systems,2019, 163:1009-1019.
[7] NIE F,WANG X,JORDAN M I,et al. The constrained Laplacian rank algorithm for graph-based clustering[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2016:1969-1976.
[8] NIE F,WU D,WANG R,et al. Self-weighted clustering with adaptive neighbors[J]. IEEE Transactions on Neural Networks and Learning Systems,2020,31(9):3428-3441.
[9] LU C,FENG J,LIN Z,et al. Subspace clustering by block diagonal representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,41(2):487-501.
[10] ZHENG A,ZHANG X,JIANG B,et al. A subspace learning approach to multishot person reidentification[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems,2020, 50(1):149-158.
[11] PEI X,WU T,CHEN C. Automated graph regularized projective nonnegative matrix factorization for document clustering[J]. IEEE Transactions on Cybernetics,2014,44(10):1821-1831.
[12] CAI D,HE X,HAN J. Locally consistent concept factorization for document clustering[J]. IEEE Transactions on Knowledge and Data Engineering,2011,23(6):902-913.
[13] NIE F,LI J,LI X. Parameter-free auto-weighted multiple graph learning:a framework for multiview clustering and semisupervised classification[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2016:1881-1887.
[14] NIE F,CAI G,LI X. Multi-view clustering and semi-supervised classification with adaptive neighbours[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto,CA:AAAI Press,2017:2408-2414.
[15] ZHAN K,ZHANG C,GUAN J,et al. Graph learning for multiview clustering[J]. IEEE Transactions on Cybernetics,2018,48(10):2887-2895.
[16] NIE F, LI J, LI X. Self-weighted multiview clustering with multiple graphs[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Palo Alto, CA:AAAI Press,2017:2564-2570.
[17] REN P,XIAO Y,XU P,et al. Robust auto-weighted multi-view clustering[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Palo Alto, CA:AAAI Press,2018:2644-2650.
[18] 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.
[19] SHAHID N, KALOFOLIAS V, BRESSON X, et al. Robust principal component analysis on graphs[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE,2015:2812-2820.
[20] XIA S,ZHANG Z,LI W,et al. GBNRS:a novel rough set algorithm for fast adaptive attribute reduction in classification[J]. IEEE Transactions on Knowledge and Data Engineering,2020(Early Access):1-1.
[21] XIA S, LIU Y, DING X, et al. Granular ball computing classifiers for efficient, scalable and robust learning[J]. Information Sciences,2019,483:136-152.
[22] XIA S,WANG G,CHEN Z,et al. Complete random forest based class noise filtering learning for improving the generalizability of classifiers[J]. IEEE Transactions on Knowledge and Data Engineering,2019,31(11):2063-2078.
[23] NIE F, WANG X, HUANG H. Clustering and projected clustering with adaptive neighbors[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM,2014:977-986.
[24] 章永来, 周耀鉴. 聚类算法综述[J]. 计算机应用,2019,39(7):1869-1882.(ZHANG Y L,ZHOU Y J. Review of clustering algorithms[J]. Journal of Computer Applications,2019,39(7):1869-1882.)
[25] LIN Z,LIU R,SU Z. Linearized alternating direction method with adaptive penalty for low-rank representation[C]//Proceedings of the 24th International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc., 2011:612-620.
[26] HEN C,QIAN H,CHEN W,et al. Auto-weighted multi-view constrained spectral clustering[J]. Neurocomputing,2019,366:1-11.
[27] CHEN M,HUANG L,WANG C,et al. Multi-view clustering in latent embedding space[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, CA:AAAI Press,2020:3513-3520.
[28] VAN DER MAATEN L,HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research,2008,9(86):2579-2605.
[29] 李杏峰, 黄玉清, 任珍文. 联合低秩稀疏的多核子空间聚类算法[J]. 计算机应用,2020,40(6):1648-1653.(LI X F,HUANG Y Q,REN Z W. Joint low-rank and sparse multiple kernel subspace clustering algorithm[J]. Journal of Computer Applications,2020,40(6):1648-1653.)

Robust multi-view clustering algorithm based on adaptive neighborhood

基于自适应邻域的鲁棒多视图聚类算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	GAO Gong, YANG Hongyu, LIU Hong. 3D point cloud face recognition based on deep learning [J]. Journal of Computer Applications, 2021, 41(9): 2736-2740.
[2]	YAN Haisheng, MA Xinqiang. Feature construction algorithm for multi-target regression via radial basis function [J]. Journal of Computer Applications, 2021, 41(8): 2219-2224.
[3]	JIANG Li, HUANG Shijian, YAN Wenjuan. Human action recognition method based on low-rank action information and multi-scale convolutional neural network [J]. Journal of Computer Applications, 2021, 41(3): 721-726.
[4]	PEI Yiyao, GUO Huiming, ZHANG Danpu, CHEN Wenbo. Robust 3D object detection method based on localization uncertainty [J]. Journal of Computer Applications, 2021, 41(10): 2979-2984.
[5]	LI Huafeng, HUANG Zhangcan, ZHANG Qiang, ZHAN Hang, TAN Qing. Improved pyramid evolution strategy for solving split delivery vehicle routing problem [J]. Journal of Computer Applications, 2021, 41(1): 300-306.
[6]	XING Zhiwei, QIAO Di, LIU Hong’en, GAO Zhiwei, LUO Xiao, LUO Qian. Optimization method of airport gate assignment based on relaxation algorithm [J]. Journal of Computer Applications, 2020, 40(6): 1850-1855.
[7]	WANG Benjie, NONG Liping, ZHANG Wenhui, LIN Jiming, WANG Junyi. 3D point cloud classification and segmentation network based on Spider convolution [J]. Journal of Computer Applications, 2020, 40(6): 1607-1612.
[8]	LI Xingfeng, HUANG Yuqing, REN Zhenwen. Joint low-rank and sparse multiple kernel subspace clustering algorithm [J]. Journal of Computer Applications, 2020, 40(6): 1648-1653.
[9]	SHI Zhiliang, CAI Wangyue, WANG Guoqiang, XIONG Linjie. Intrinsic shape signature algorithm based on adaptive neighborhood [J]. Journal of Computer Applications, 2020, 40(4): 1151-1156.
[10]	SONG Yan, YIN Jun. Multi-view spectral clustering algorithm based on shared nearest neighbor [J]. Journal of Computer Applications, 2020, 40(11): 3211-3216.
[11]	ZHANG Li, KONG Xu, SUN Zhonggui. Matrix completion algorithm based on nonlocal self-similarity and low-rank matrix approximation [J]. Journal of Computer Applications, 2020, 40(11): 3327-3331.
[12]	LAI Jie, WANG Xiaodan, LI Rui, ZHAO Zhenchong. Denoising autoencoder based extreme learning machine [J]. Journal of Computer Applications, 2019, 39(6): 1619-1625.
[13]	HUANG Guangqiu, XIE Rong. Cascading failure model of carbon emission spatial correlation system considering load overload [J]. Journal of Computer Applications, 2019, 39(6): 1829-1835.
[14]	CHEN Lixia, LIU Junli, WANG Xuewen. Foreground detection with weighted Schatten-p norm and 3D total variation [J]. Journal of Computer Applications, 2019, 39(4): 1170-1175.
[15]	YU Jianglan, LI Xiangli, ZHAO Pengfei. Sparse non-negative matrix factorization based on kernel and hypergraph regularization [J]. Journal of Computer Applications, 2019, 39(3): 742-749.