Abstract
This paper develops a weak-label-based global and local multi-view multi-label learning with three-way clustering (WL-GLMVML-ATC) to solve multi-view multi-label data sets and exploit more authentic global and local label correlations of both the whole data set and each view simultaneously. Different from the traditional learning methods, WL-GLMVML-ATC pays more attention to the solutions of weak-label cases and uncertain relationships of clusters with the usage of Universum and active three-way clustering. According to Universum notion, even though the size of labeled instances is much more smaller than the unlabeled ones, the useful sample information can still be enhanced. Through the active three-way clustering strategy, the belongingness of instances to a cluster depend on the probabilities of uncertain instances belonging to core regions. This strategy brings a more authentic local label correlation since many traditional methods suppose that instances and the corresponding clusters always exhibit certain relationships such as belong-to definitely and not belong-to definitely. This hypothesis is not ubiquitous in real-world applications. According to the experiments, we can see WL-GLMVML-ATC (1) achieves a better performance, be superior to the classical multi-view learning methods and multi-label learning methods in statistical, advances the development of these learning methods in final; (2) won’t add too much running time; (3) has a good convergence and ability to process multi-view multi-label data sets.
Similar content being viewed by others
Notes
References
Liu JL, Teng SH, Fei LK, Zhang W, Fang XZ, Zhang ZX, Wu NQ (2021) A novel consensus learning approach to incomplete multi-view clustering. Pattern Recognit 115:107890
Tarekegn A, Giacobini M, Michalak K (2021) A review of methods for imbalanced multi-label classification. Pattern Recognit. https://doi.org/10.1016/j.patcog.2021.107965
Hu SZ, Yan XQ, Ye YD (2020) Dynamic auto-weighted multi-view co-clustering. Pattern Recognit 99:107101
Yu GX, Xing YY, Wang J, Domeniconi C, Zhang XL (2021) Multiview multi-instance multilabel active learning. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3056436
Zhu Y, Kwok JT, Zhou ZH (2017) Multi-label learning with global and local label correlation. IEEE Trans Knowl Data Eng 99:1–24
Tan QY, Yu GX, Wang J, Domeniconi C, Zhang XL (2021) Individuality- and commonality-based multiview multilabel learning. IEEE Trans Cybern 51(3):1716–1727
Yin QY, Zhang JG, Wu S, Li HX (2019) Multi-view clustering via joint feature selection and partially constrained cluster label learning. Pattern Recognit 93:380–391
Zhu CM, Miao DQ, Wang Z, Zhou RG, Wei L, Zhang XF (2020) Global and local multi-view multi-label learning. Neurocomputing 371:67–77
Yao YY (2012) An outline of a theory of three-way decisions. In: Proceedings of the 8th international conference on rough sets and current trends in computing (RSCTC 2012), pp 1–17
Yao YY (2016) Three-way decisions and cognitive computing. Cogn Comput 8(4):543–554
Yu H (2017) A framework of three-way cluster analysis. In: Proceedings of the international joint conference on rough sets (IJCRS 2017), pp 300–312
Yu H, Jiao P, Yao YY, Wang GY (2016) Detecting and refining overlapping regions in complex networks with three-way decisions. Inf Sci 373:21–41
Chu XL, Sun BZ, Li X, Han KY, Wu JQ, Zhang Y, Huang QC (2020) Neighborhood rough set-based three-way clustering considering attribute correlations: an approach to classification of potential gout groups. Inf Sci 535:28–41
Das P, Das AK, Nayak J, Pelusi D, Ding WP (2019) Group incremental adaptive clustering based on neural network and rough set theory for crime report categorization. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.10.109
Zhao B, Ren Y, Gao DK, Xu LZ (2019) Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network. Appl Soft Comput 78:132–140
Zhou J, Lai ZH, Miao DQ, Gao C, Yue XD (2020) Multigranulation rough-fuzzy clustering based on shadowed sets. Inf Sci 507:553–573
Ubukata S, Notsu A, Honda K (2021) Objective function-based rough membership C-means clustering. Inf Sci 548:479–496
Zhang PF, Li TR, Wang GQ, Luo C, Chen HM, Zhang JB, Wang DX, Yu Z (2021) Multi-source information fusion based on rough set theory: a review. Inf Fus 68:85–117
Zhao J, Liang JM, Dong ZN, Tang DY, Liu Z (2020) Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes. Pattern Recognit 107:107517
Roy S, Maji P (2020) Rough segmentation of coherent local intensity for bias induced 3-D MR brain images. Pattern Recognit 97:106997
Zhao Y, Luo ZW, Quan CQ, Liu DC, Wang G (2020) Cluster-wise learning network for multi-person pose estimation. Pattern Recognit 98:107074
Zhang TF, Ma FM, Yue D, Peng C, O’Hare GMP (2020) Interval type-2 fuzzy local enhancement based rough k-means clustering considering imbalanced clusters. IEEE Transactions on Fuzzy Systems 28(9):1925–1939
Arnold SD, Radu DG, Horia FP, Costel S (2019) A comparison study of similarity measures in rough sets clustering. In: 2019 IEEE 15th international scientific conference on informatics (ISCI 2019), pp 37–42
Feng YF, Chen HM (2019) An improved density peaks clustering based on rough set theory for overlapping community detection. In: 2019 IEEE 14th international conference on intelligent systems and knowledge engineering (ISKE 2019), pp 21–28
Roy S, Maji P (2020) Medical image segmentation by partitioning spatially constrained fuzzy approximation spaces. IEEE Trans Fuzzy Syst 28(5):965–977
Li DW, Zhang HQ, Li TR, Bouras A, Yu X, Wang T (2021) Hybrid missing value imputation algorithms using fuzzy c-means and vaguely quantified rough set. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2021.3058643
Gao C, Zhou J, Miao DQ, Wen JJ, Yue XD (2021) Three-way decision with co-training for partially labeled data. Inf Sci 544:500–518
Yu H, Wang XC, Wang GY, Zeng XH (2020) An active three-way clustering method via low-rank matrices for multi-view data. Inf Sci 507:823–839
Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: Proceedings of the 7th international conference on machine learning (ICML 2000), pp 1103–1110
Klein D, Kamvar SD, Manning CD (2002) From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. Technical Report, Stanford
Basu S, Banerjee A, Mooney RJ (2004) Active semi-supervision for pairwise constrained clustering. In: Proceedings of the 4th SIAM international conference on data mining (SDM 2004), pp 333–344
Mallapragada PK, Jin R, Jain AK (2008) Active query selection for semi-supervised clustering. In: Proceedings of the 19th international conference on pattern recognition (ICPR 2008), pp 1–4
Vapnik V, Kotz S (1982) Estimation of dependences based on empirical data. Springer, Berlin
Zhu CM, Miao DQ, Zhou RG, Wei L (2020) Weight-and-Universum-based semi-supervised multi-view learning machine. Soft Comput 24(14):10657–10679
Wang Z, Hong SS, Yao LJ, Li DD, Du WL, Zhang J (2020) Multiple Universum empirical kernel learning. Eng Appl Artif Intell 89:103461
Richhariya B, Tanveer M (2020) A reduced universum twin support vector machine for class imbalance learning. Pattern Recognit 102:107150
Liu CL, Hsaio WH, Lee CH, Chang TH, Kuo TH (2016) Semi-supervised text classification with Universum learning. IEEE Trans Cybern 46(2):462–473
Zhang CQ, Yu ZW, Hu QH, Zhu PF, Liu XW, Wang XB (2018) Latent semantic aware multi-view multi-label classification. In: Thirty-second AAAI conference on artificial intelligence, pp 4414–4421
Zhang J, Li CD, Cao DL, Lin YJ, Su SZ, Dai L, Li SZ (2018) Multi-label learning with label-specific features by resolving label correlations. Knowl Based Syst 159:148–157
Huang J, Qin F, Zheng X, Cheng ZK, Yuan ZX, Zhang WG, Huang QM (2019) Improving multi-label classification with missing labels by learning label-specific features. Inf Sci 492:124–146
Chua TS, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) Nus-wide: a real-world web image database from national university of Singapore. In: Proceedings of the ACM international conference on image and video retrieval, p 48
He ZY, Chen C, Bu JJ, Li P, Cai D (2015) Multi-view based multi-label propagation for image annotation. Neurocomputing 168:853–860
Sun SL, Zhang QJ (2011) Multiple-view multiple-learner semi-supervised learning. Neural Process Lett 34:229–240
Zhang CQ, Fu HZ, Hu QH, Cao XC, Xie Y, Tao DC, Xu D (2018) Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2018.2877660
Wu F, Jing XY, You XG, Yue D, Hu RM, Yang JY (2016) Multi-view low-rank dictionary learning for image classification. Pattern Recognit 50:143–154
Weng W, Lin YJ, Wu SX, Li YW, Kang Y (2018) Multi-label learning based on label-specific features and local pairwise label correlation. Neurocomputing 273:385–394
Kumar V, Pujari AK, Padmanabhan V, Sahu SK, Kagita VR (2018) Multi-label classification using hierarchical embedding. Expert Syst Appl 91:263–269
Qian BY, Wang X, Ye JP, Davidson I (2015) A reconstruction error based framework for multi-label and multi-view learning. IEEE Trans Knowl Data Eng 27(3):594–607
Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1–30
Acknowledgements
This work is sponsored by ‘Chenguang Program’ supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under grant number 18CG54. This work is also supported by Project funded by China Postdoctoral Science Foundation under grant number 2019M651576, National Natural Science Foundation of China (CN) under grant number 61602296, Natural Science Foundation of Shanghai under grant number 16ZR1414500. The authors would like to thank their supports.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhu, C., Cao, D., Guo, S. et al. Weak-label-based global and local multi-view multi-label learning with three-way clustering. Int. J. Mach. Learn. & Cyber. 13, 1337–1354 (2022). https://doi.org/10.1007/s13042-021-01450-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-021-01450-1