Skip to main content
SpringerLink
Log in

Joint label completion and label-specific features for multi-label learning algorithm

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Label correlations have always been one of the hotspots of multi-label learning. Using label correlations to complete the original label can enrich the information of the label matrix. At the same time, label-specific features give a thought that different labels have inherent characteristics that can be distinguished, and we can use label correlations to enhance the learning process of label-specific features among similar labels. At present, most of the algorithms combine label correlations and label-specific features to improve the multi-label learning effect, but do not consider the impact of label marking errors or defaults in data sets. In fact, the label completion method can further enrich the information of label matrix, and then the joint learning framework of joint label-specific features can effectively improve the robustness of the multi-label learning algorithm. Based on this, this paper proposes a multi-label learning algorithm for joint label completion and label-specific features, and constructs a new multi-label learning algorithm framework by means of joint label completion and label-specific features. Completion matrix and label-specific features are obtained by alternating iteration method, and the label matrix updating the optimization framework fully considers the label correlations. The algorithm in this paper has been demonstrated and trained on several benchmark multi-label data sets by extensive experiments, which verifies the effectiveness of the algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Beck A, Teboulle M (2009a) Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans Image Process 18(11):2419–2434

    Article  MathSciNet  Google Scholar 

  • Beck A, Teboulle M (2009b) A fast iterative shrinkage–thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202

    Article  MathSciNet  Google Scholar 

  • Chen M, Zheng A, Weinberger K (2013) Fast image tagging. In: International conference on machine learning, pp 1274–1282

  • Cheng YS, Zhao DW, Zhan WF et al (2018) Multi-label learning of non-equilibrium labels completion with mean shift. Neurocomputing 321:92–102

    Article  Google Scholar 

  • Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  • Dong HC, Li YF, Zhou ZH (2018) Learning from semi-supervised weak-label data. In: Thirty-second AAAI conference on artificial intelligence, pp 2926–2933

  • Elisseeff A, Weston J (2002) A kernel method for multi-labelled classification. In: Advances in neural information processing systems, pp 681–687

  • Fu B, Xu G, Wang Z, et al (2013) Leveraging supervised label dependency propagation for multi-label learning. In: 2013 IEEE 13th international conference on data mining. IEEE, pp 1061–1066

  • Guo K, Cao R, Kui X et al (2019) LCC: towards efficient label completion and correction for supervised medical image learning in smart diagnosis. J Netw Comput Appl 133:51–59

    Article  Google Scholar 

  • Han H, Huang M, Zhang Y et al (2019) Multi-label learning with label specific features using correlation information. IEEE Access 7:11474–11484

    Article  Google Scholar 

  • He ZF, Yang M, Gao Y et al (2019) Joint multi-label classification and label correlations with missing labels and feature selection. Knowl Based Syst 163:145–158

    Article  Google Scholar 

  • Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  • Huang J, Li G, Huang Q, et al (2015) Learning label specific features for multi-label classification. In: 2015 IEEE international conference on data mining. IEEE, pp 181–190

  • Huang J, Li G, Huang Q et al (2016) Learning label-specific features and class-dependent labels for multi-label classification. IEEE Trans Knowl Data Eng 28(12):3309–3323

    Article  Google Scholar 

  • Huang J, Li G, Huang Q et al (2018) Joint feature selection and classification for multilabel learning. IEEE Trans Cybern 48(3):876–889

    Article  Google Scholar 

  • Huang J, Qin F, Zheng X et al (2019) Improving multi-label classification with missing labels by learning label-specific features. Inf Sci 492:124–146

    Article  MathSciNet  Google Scholar 

  • Liu G, Lin Z, Yu Y (2010) Robust subspace segmentation by low-rank representation. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 663–670

  • Liu B, Li Y, Xu Z (2018) Manifold regularized matrix completion for multi-label learning with ADMM. Neural Netw 101:57–67

    Article  Google Scholar 

  • Nguyen TT, Nguyen TTT, Luong AV et al (2019) Multi-label classification via label correlation and first order feature dependance in a data stream. Pattern Recogn 90:35–51

    Article  Google Scholar 

  • Rodriguez P, Wohlberg B (2016) Incremental principal component pursuit for video background modeling. J Math Imaging Vis 55(1):1–8

    Article  MathSciNet  Google Scholar 

  • Sitompul OS, Nababan EB (2018) Biased support vector machine and weighted-smote in handling class imbalance problem. Int J Adv Intell Inform 4(1):21–27

    Article  Google Scholar 

  • Sun YY, Zhang Y, Zhou ZH (2010) Multi-label learning with weak label. In: Twenty-fourth AAAI conference on artificial intelligence, pp 593–598

  • Wu B, Lyu S, Ghanem B (2015) ML–MG: multi-label learning with missing labels using a mixed graph. In: Proceedings of the IEEE international conference on computer vision, pp 4157–4165

  • Xu M, Jin R, Zhou ZH (2013) Speedup matrix completion with side information: application to multi-label learning. In: Advances in neural information processing systems, pp 2301–2309

  • Xu L, Wang Z, Shen Z, et al (2014) Learning low-rank label correlations for multi-label classification with missing labels. In: 2014 IEEE international conference on data mining. IEEE, pp 1067–1072

  • Xu S, Yang X, Yu H et al (2016) Multi-label learning with label-specific feature reduction. Knowl Based Syst 104:52–61

    Article  Google Scholar 

  • Zendehboudi A, Baseer MA, Saidur R (2018) Application of support vector machine models for forecasting solar and wind energy resources: a review. J Clean Prod 199:272–285

    Article  Google Scholar 

  • Zhang ML, Wu L (2015) Lift: multi-label learning with label-specific features. IEEE Trans Pattern Anal Mach Intell 37(1):107–120

    Article  Google Scholar 

  • Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  Google Scholar 

  • Zhang J, Li C, Cao D et al (2018) Multi-label learning with label-specific features by resolving label correlations. Knowl Based Syst 159:148–157

    Article  Google Scholar 

  • Zhao D, Wang T, Chu F (2019) Deep convolutional neural network based planet bearing fault classification. Comput Ind 107:59–66

    Article  Google Scholar 

  • Zhu Y, Kwok JT, Zhou ZH (2017) Multi-label learning with global and local label correlation. IEEE Trans Knowl Data Eng 30(6):1081–1094

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, 246011, Anhui, China

    Yibin Wang, Weijie Zheng, Yusheng Cheng & Dawei Zhao

  2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing, 246133, China

    Yibin Wang & Yusheng Cheng

Authors
  1. Yibin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weijie Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yusheng Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dawei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yusheng Cheng.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Communicated by A. Di Nola.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Zheng, W., Cheng, Y. et al. Joint label completion and label-specific features for multi-label learning algorithm. Soft Comput 24, 6553–6569 (2020). https://doi.org/10.1007/s00500-020-04775-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04775-1

Keywords

Search

Navigation