Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (2): 34-46.doi: 10.6040/j.issn.1672-3961.0.2020.234

Previous Articles    

Global and local multi-view multi-label learning with active three-way clustering

ZHU Changming, YUE Wen, WANG Panhong, SHEN Zhenyu, ZHOU Rigui   

  1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Published:2021-04-16

PDF (PC)

696

Abstract: In order to consider the uncertain belongingness relationship between instances and clusters and then extend the application scopes of global and local multi-view multi-label learning, an algorithm of global and local multi-view multi-label learning machine with active three-way clustering(GLMVML-ATC)was proposed. With the usage of active three-way clustering strategy, the belongingness of instances to a cluster depended on the probabilities of uncertain instances belonging to core regions. This made local label correlations more authentic, which enhanced the performances of multi-view multi-label learning machines further and accelerated their development. Experimental results validated that GLMVML-ATC improved the classification performances with 3% at least, while the added training time less than 7%. It was superior to the classical multi-view learning machines and multi-label learning machines.

Key words: three-way clustering, multi-view, multi-label, label correlation, global and local

CLC Number: 

  • TP391
[1] FU Y J, LIU J M, LI X L, et al. Service usage analysis in mobile messaging apps: a multi-label multi-view perspective[C] //IEEE International Conference on Data Mining. Barcelona, Spain: IEEE Xplore, 2016: 877-882.
[2] YIN Q Y, ZHANG J G, WU S, et al. Multi-view clustering via joint feature selection and partially constrained cluster label learning[J]. Pattern Recognition, 2019, 93(19):380-391.
[3] WANG G X, ZHANG C Q, ZHU P F, et al. Semisupervised multi-view multi-label classification based on nonnegative matrix factorization[C] //International Conference on Artificial Neural Networks and Machine Learning. Alghero, Italy: Springer, 2017: 340-348.
[4] MAEDA K, TAKAHASHI S, OGAWA T, et al. Multi-feature fusion based on supervised multi-view multilabel canonical correlation projection[C] //IEEE International Conference on Acoustics, Speech and Signal Processing. Brughton, UK: IEEE Xplore, 2019: 3936-3940.
[5] ZOU F H, LIU Y, WANG H, et al. Multi-view multi-label learn-ing for image annotation [J]. Multimedia Tools and Applications, 2016, 75(20): 12627-12644.
[6] YAO Y Y. An outline of a theory of three-way decisions[C] //Proceed-ings of the 8th International Conference on Rough Sets and Current Trends in Computing. Heidelberg, Germany: Springer, 2012: 1-17.
[7] YAO Y Y. Three-way decisions and cognitive computing[J]. Cognitive Computation, 2016, 8(4):543-554.
[8] YU H, JIAO P, YAO Y Y, et al. Detecting and refining overlapping regions in complex networks with threeway decision[J]. Information Sciences, 2016, 373(23):21-41.
[9] YU H. A framework of three-way cluster analysis[C] //Proceedings of the International Joint Conference on Rough Sets. Olsztyn, Poland: Springer, 2017: 300-312.
[10] ZHU C M, MIAO D Q, WANG Z, et al. Global and local multi-view multi-label learning [J]. Neurocomputing, 2020, 371(32): 67-77.
[11] YU H, WANG X C, WANG G Y, et al. An active three-way clustering method via low-rank matrices for multi-view data[J]. Information Sciences, 2020, 507(27): 823-839.
[12] BASU S, BANERJEE A, MOONEY R. Active semi-supervision for pairwise constrained clustering[C] //Proceedings of the 4th SIAM International Conference on Data Mining. Lake Buena Vista, USA: SIAM, 2004: 333-344.
[13] MALLAPRAGADA P, JIN R, JAIN A. Active query selection for semi-supervised clustering[C] //Proceedings of the 19th International Conference on Pattern Recognition. Tampa, USA: IEEE Explore, 2008: 1-4.
[14] LUO D J, DING C, HUANG H, et al. Non-negative Laplacian embedding[C] //Proceedings of the 9th IEEE International Conference on Data Mining(ICDM). Miami, USA: IEEE Explore, 2009: 337-346.
[15] ZHANG C Q, YU Z W, HU Q H, et al. Latent semantic aware multi-view multi-label classification[C] //32th AAAI Conference on Artificial Intelligence. New Orleans, USA: AAAI Press, 2018: 4414-4421.
[16] ZHANG C Q, FU H Z, HU Q H, et al. Generalized latent multi-view subspace clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(1): 86-99.
[17] ZHANG J, LI C D, CAO D L, et al. Multi-label learning with label-specific features by resolving label correlations[J]. Knowledge-Based Systems, 2018, 159:148-157.
[18] HUANG J, QIN F, ZHENG X, et al. Improving multi-label classification with missing labels by learning label-specific feature[J]. Information Sciences, 2019, 492(26):124-146.
[19] CHUA T S, TANG J H, HONG R C, et al. Nus-wide: a real-world web image database from national university of Singapor[C] //Proceedings of the ACM International Conference on Image and Video Retrieval. Santorini Island, Greece: ACM, 2009: 368-376.
[20] HE Z Y, CHEN C, BU J J, et al. Multi-view based multi-label propagation for image annotation[J]. Neurocomputing, 2015, 168(27):853-860.
[21] SUN S L, ZHANG Q J. Multiple-view Multiple-learner Semi-supervised Learning[J]. Neural Processing Letters, 2011, 34:229-240.
[22] WU F, JING X Y, YOU X G, et al. Multi-view low-rank dictionary learning for image classification[J]. Pattern Recognition, 2016, 50(16):143-154.
[23] WENG W, LIN Y J, WU S X, et al. Multilabel learning based on label-specific features and local pairwise label correlation[J]. Neurocomputing, 2018, 273(30):385-394.
[24] KUMAR V, PUJARI A, PADMANSBHAN V, et al. Multi-label classification using hierarchical embedding[J]. Expert Systems with Applications, 2018, 91:263-269.
[25] ZHU Y, KWOK J, ZHOU Z H. Multi-Label Learning with Global and local label correlation[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 6(30):1081-1094.
[26] QIAN B Y, WANG X, YE J P, et al. A reconstruction error based framework for multi-label and multi-view learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(3):594-607.
[1] ZHANG Peirui, YANG Yan, XING Huanlai, YU Xiuying. Incremental multi-view clustering algorithm based on kernel K-means [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 48-53.
[2] GUO Chao, YANG Yan, JIANG Yongquan, SONG Yi. Condition recognition of high-speed train based on multi-view classification ensemble [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(1): 7-14.
[3] GAO Shuang1,2, ZHANG Hua-xiang1,2*, FANG Xiao-nan1,2. Independent component analysis and co-training based Web spam detection [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(2): 29-34.
[4] LI Ya-lin1,2, ZHANG Hua-xiang1,2*, FENG Xin-ying1,2. A new multi-label learning algorithm based on semi-supervised learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(2): 18-22.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd