Skip to main content
Springer Nature Link
Log in

Multi-view multi-label learning for label-specific features via GLocal Shared Subspace Learning

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In multi-label learning (MLL), label-specific feature (LSF) learning assumes that labels are determined by their inherent characteristics. However, in multi-view multi-label learning (MVMLL), the heterogeneity problem persists within the feature space. The views with varying dimensions can result in different dimensions of extracted LSF. Existing algorithms extract the LSF for each view separately, suffering the inadequate communication of the LSF and poor classification accuracy. The subspace learning method can address the dimension-inconsistency problem in multi-views by extracting extract the shared subspace for each view by substituting the original view feature space. However, the individual subspaces contain relatively homogeneous information. Based on this analysis, the GLocal Shared Subspace Learning (GLSSL) algorithm was proposed for multi-view multi-label learning to access more informative subspaces. First, the label groups were obtained through spectral clustering, entirely considering the correlation between the label groups and features to identify the specific relevant view features corresponding to each label group. Subsequently, the global shared subspace (global subspace) and local shared subspace (local subspace) were extracted from the original feature space and feature sets, respectively. Finally, the local subspace was complemented with the global subspace for LSF learning. The proposed algorithm was validated through comparative experiments with several state-of-the-art algorithms on multiple benchmark multi-view multi-label datasets.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The experimental data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Lyu GY, Deng X, Wu YN, Feng SH (2022) Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning. Proc AAAI Conf Artif Intell 36:7647–7654

    Google Scholar 

  2. Yin J, Zhang WT (2023) Multi-view multi-label learning with double orders manifold preserving. Appl Intell 53:14703–14716

    Article  Google Scholar 

  3. Chen ZS, Wu X, Chen QG, Hu Y, Zhang ML (2020) Multi-view partial multi-label learning with graph-based disambiguation. Proceedings of the AAAI conference on artificial intelligence, Hilton New York Midtown, New York, USA, pp 3553–3560

    Google Scholar 

  4. Huang J, Qu XW, Li GR, Qin F, Zheng X, Huang QM (2019) Multi-view multi-label learning with view-label-specific features. IEEE access 7:100979–100992

    Article  Google Scholar 

  5. Zhao DW, Gao QW, Lu YX, Sun D (2021) Two-step multi-view and multi-label learning with missing label via subspace learning. Appl Soft Comput 102:107120

    Article  Google Scholar 

  6. Liu W, Yuan JZ, Lyu G, Feng SH (2023) Label driven latent subspace learning for multi-view multi-label classification. Appl Intell 53:3850–3863

    Article  Google Scholar 

  7. Wen J, Liu CL, Deng SJ, Liu YC, Fei LK, Yan K, Xu Y (2023) Deep double incomplete multi-view multi-label learning with incomplete labels and missing views. IEEE Transact Neur Netw Learn Syst, p 1–13. https://doi.org/10.1109/TNNLS.2023.3260349

  8. Wang YN, Guo Y, Wang Z, Wang F (2024) Joint learning of latent subspace and structured graph for multi-view clustering. Pattern Recogn 154:110592

    Article  Google Scholar 

  9. Liu B, Li WB, Xiao YS, Chen XD, Liu LW, Liu CD, Wang K, Sun P (2023) Multi-view multi-label learning with high-order label correlation. Inf Sci 624:165–184

    Article  Google Scholar 

  10. Li DY, Zhang SY, Ma XK (2022) Dynamic Module Detection in Temporal Attributed Networks of Cancers. IEEE/ACM Trans Comput Biol Bioinf 19(4):2219–2230

    Article  Google Scholar 

  11. Ma XK, Zhao W, Wu WM (2023) Layer-Specific Modules Detection in Cancer Multi-Layer Networks. IEEE/ACM Trans Comput Biol Bioinf 20(2):1170–1179

    Article  Google Scholar 

  12. Gao XW, Wang Y, Hou WM, Liu ZY, Ma XK (2023) Multi-View Clustering for Integration of Gene Expression and Methylation Data with Tensor Decomposition and Self-Representation Learning. IEEE/ACM Trans Comput Biol Bioinf 20(3):2050–2063

    Article  Google Scholar 

  13. Gao XW, Ma XK, Zhang WS, Huang JB, Li H, Li YN, Cui JT (2022) Multi-View Clustering with Self-Representation and Structural Constraint. IEEE Transact Big Data 8(4):882–893

    Article  Google Scholar 

  14. Huang ZH, Wang Y, Ma XK (2022) Clustering of Cancer Attributed Networks by Dynamically and Jointly Factorizing Multi-Layer Graphs. IEEE/ACM Trans Comput Biol Bioinf 19(5):2737–2748

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Huang J, Li GR, Huang QM, Wu XD (2015) Learning label specific features for multi-label classification, 2015 IEEE International Conference on Data Mining, Atlantic City, NJ, USA, p 181–190

  17. Wang YB, Pei GS, Cheng YS (2020) Group-label-specific features learning method based on label-density classification margin. J Electron Inf Technol 42(5):1179–1187

    Google Scholar 

  18. 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 

  19. Zhao DW, Gao QW, Lu YX, Sun D (2022) Learning multi-label label-specific features via global and local label correlations. Soft Comput 26:2225–2239

    Article  Google Scholar 

  20. Zhang P, Gao WF, Hu JC, Li YH (2021) Multi-label feature selection based on the division of label topics. Inf Sci 553:129–153

    Article  MathSciNet  Google Scholar 

  21. Zhou HF, Wang XQ, Zhou RR (2022) Feature selection based on mutual information with correlation coefficient. Appl Intell 52:5457–5474

    Article  Google Scholar 

  22. Hu L, Gao LB, Li YH, Zhang P, Gao HF (2022) Feature-specific mutual information variation for multi-label feature selection. Inf Sci 593:449–471

    Article  Google Scholar 

  23. Cevikalp H, Larlus D, Douze M, Jurie M (2007) Local subspace classifiers: linear and nonlinear approaches, 2007 IEEE workshop on machine learning for signal processing, Thessaloniki, Greece, p 57–62

  24. Miao JL, Wang YB, Cheng YS, Chen F (2023) Parallel dual-channel multi-label feature selection 27:7115–7130

    Google Scholar 

  25. Wu X, Chen QG, Hu Y, Wang DB, Chang XD (2019) Multi-view multi-label learning with view-specific information extraction. Proceedings of the twenty-eighth international joint conference on artificial intelligence, Macao, China, pp 3884–3890

    Google Scholar 

  26. Cheng YS, Li QY, Wang YB, Zheng WJ (2022) Multi-view multi-label learning with view feature attention allocation. Neurocomputing 501:857–874

    Article  Google Scholar 

  27. Liu C, Wen J, Liu Y et al (2024) Masked two-channel decoupling framework for incomplete multi-view weak multi-label learning. Adv Neural Inf Process Syst 36:32387–32400

    Google Scholar 

  28. 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

    Article  Google Scholar 

  29. Shen XB, Tang YP, Zheng YH, Yuan YH, Sun QS (2022) Unsupervised Multiview Distributed Hashing for Large-Scale Retrieval. IEEE Trans Circuits Syst Video Technol 32(12):8837–8848

    Article  Google Scholar 

  30. Gao WF, Hao PT, Wu Y, Zhang P (2023) A unified low-order information-theoretic feature selection framework for multi-label learning. Pattern Recogn 134:109111

    Article  Google Scholar 

  31. Shen XB, Dong GH, Zheng YH, Lan L, Tsang IW, Sun QS (2022) Deep Co-Image-Label Hashing for Multi-Label Image Retrieval. IEEE Trans Multimed 24:1116–1126

    Article  Google Scholar 

  32. Beck A, Teboulle M (2009) 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 

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

    Article  MathSciNet  Google Scholar 

  34. Wang ZY, Xu YT (2024) A two-stage multi-view partial multi-label learning for enhanced disambiguation. Knowl-Based Syst 293:111680

    Article  Google Scholar 

  35. Feng L, Huang J, Shu SL, An B (2022) Regularized matrix factorization for multilabel learning with missing labels. IEEE Transact Cybern 52(5):3710–3721

    Article  Google Scholar 

  36. Zhao DW, Gao QW, Lu YX, Sun D, Cheng YS (2021) Consistency and diversity neural network multi-view multi-label learning. Knowl-Based Syst 218:106841

    Article  Google Scholar 

  37. Tian X, Zhao C, Liu CL, Wen J, Tang ZY (2024) A two-stage information extraction network for incomplete multi-view multi-label classification. 2024 Proc AAAI Conf Artif Intell Vancouver British Columbia 38:15249–15257

    Google Scholar 

  38. Ge WX, Wang YB, Xu YT, Cheng YS (2024) Causality-driven intra-class non-equilibrium label-specific features learning. Neural Process Lett 56:120

    Article  Google Scholar 

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

    MathSciNet  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of Anhui under Grant (No. 2108085MF216), the major projects of Anhui Provincial Department of Education (Intelligent Control of Pressure Casting based on Digital Twins) and the key projects of Anhui Provincial Department of Education (Multi-label Data Classification Modeling and Application Research in Open Environment).

Author information

Author notes

  1. Yusheng Cheng and Yuting Xu contributed to the work equally, and they are co-first authors.

Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, 246133, China

    Yusheng Cheng, Yuting Xu & Wenxin Ge

  2. School of Computing and the Arts, Anhui Technical College of Industry and Economy, Hefei, 230051, China

    Yuting Xu

  3. School of Intelligence and Electrical Engineering, Huainan Vocational Technical College, Huainan, 232001, China

    Wenxin Ge

Authors
  1. Yusheng Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yuting Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wenxin Ge
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

The authors confirm contribution to the paper as follows: Yusheng Cheng: Supervision,Methodology, Writing-Original draft preparation; Yuting Xu: Conceptualization, Software, Validation; WenxinGe:Writing-Original draft preparation.

Corresponding author

Correspondence to Yusheng Cheng.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Ethical approval

This manuscript does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, Y., Xu, Y. & Ge, W. Multi-view multi-label learning for label-specific features via GLocal Shared Subspace Learning. Appl Intell 54, 11054–11067 (2024). https://doi.org/10.1007/s10489-024-05779-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-024-05779-2

Keywords