[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Multi-view subspace enhanced representation of manifold regularization and low-rank tensor constraint

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

In this paper, to extract the manifold information from multi-view data and enhance the clustering performance of a multi-view learning method, the multi-view subspace enhanced representation of manifold regularization and low-rank tensor constraint (MSERMLRT) method is introduced. Our model uses a tensor to explore the correlation between views. The tensor is constrained with a low-rank, and the purpose of such processing is to reduce the redundant information of the learned subspace representation. This model also uses the manifold information from multi-view data and imposes a sparse constraint on the product of itself and the transpose of the subspace representation matrix to enhance the diagonal block structure of the subspace representation, thereby improving its clustering effect to a certain extent. We also designed a helpful method for solving the MSERMLRT model and analyzed the convergence of our approach both theoretically and experimentally. The clustering performance on certain challenging datasets indicate that the MSERMLRT model is superior to many other advanced multi-view clustering methods.

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
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

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

Data availability

All data generated or analysed during this study are included in this published article.

References

  1. Yin H, Hu W, Li F et al (2021) One-step multi-view spectral clustering by learning common and specific nonnegative embeddings. Int J Mach Learn Cybern 12(7):2121–2134

    Article  Google Scholar 

  2. Chen Y, Wang S, Peng C et al (2021) Generalized nonconvex low-rank tensor approximation for multi-view subspace clustering. IEEE Trans Image Process 30:4022–4035

    Article  MathSciNet  Google Scholar 

  3. Zhu X, Guo J, Nejdl W et al (2020) Multi-view image clustering based on sparse coding and manifold consensus. Neurocomputing 403(12):53–62

    Article  Google Scholar 

  4. Sun Y, Li L, Zheng L et al (2019) Image classification base on PCA of multi-view deep representation. J Vis Commun Image Represent 62:253–258

    Article  Google Scholar 

  5. Zhang C, Cheng J, Tian Q (2019) Multi-view image classification with visual, semantic and view consistency. IEEE Trans Image Process 99:617–627

    MathSciNet  MATH  Google Scholar 

  6. Li X, Monga V, Mahalanobis A (2020) Multi-view automatic target recognition for infrared imagery using collaborative sparse priors. IEEE Trans Geosci Remote Sens 99:1–15

    Google Scholar 

  7. Hui K, Ganaa ED, Zhan YZ, Shen XJ (2021) Robust deflated canonical correlation analysis via feature factoring for multi-view image classification. Multimed Tools Appl 80(16):24843–24865

    Article  Google Scholar 

  8. Guo Y, Ji J, Shi D et al (2021) Multi-view feature learning for VHR remote sensing image classification. Multimed Tools Appl 80(15):23009–23021

    Article  Google Scholar 

  9. Kundu A, Yin X, Fathi A, Ross D, Brewington B, Funkhouser T, Pantofaru C (2020) Virtual multi-view fusion for 3d semantic segmentation. In: European Conference on Computer Vision, vol 12369, pp 518–535

  10. Liu Q, Kampffmeyer M C, Jenssen R, et al (2020) Multi-view self-constructing graph convolutional networks with adaptive class weighting loss for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition Workshops, 2020, pp 44–45

  11. Gerdzhev M, Razani R, Taghavi E liu BB (2021) Tornado-net: multi-view total variation semantic segmentation with diamond inception module. In: 2021 IEEE International Conference on Robotics and Automation, ICRA, pp 9543–9549

  12. Song K, Zhao Z, Wang J, Qiang Y, Zhao J, Bilal Zia M (2022) Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer. Int J Mach Learn Cybern 13(5):1283–1299

    Article  Google Scholar 

  13. Pan G, Xiao L, Bai Y et al (2020) Multi-view diffusion map improves prediction of fluid intelligence with two paradigms of fMRI analysis. IEEE Trans Biomed Eng 68(8):2529–2539

    Article  Google Scholar 

  14. Avants BB, Tustison NJ, Stone JR (2021) Similarity-driven multi-view embeddings from high-dimensional biomedical data. Nat Comput Sci 1(2):143–152

    Article  Google Scholar 

  15. García-Martínez C, Ventura S (2020) Multi-view genetic programming learning to obtain interpretable rule-based classifiers for semi-supervised contexts. Lessons Learnt. Int J Comput Intell Syst 13(1):576–590

    Article  Google Scholar 

  16. Elhamifar E, Vidal R (2013) Sparse subspace clustering: Algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781

    Article  Google Scholar 

  17. Liu G, Lin Z, Yan S et al (2012) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184

    Article  Google Scholar 

  18. Wang S, Yuan X, Yao T, et al (2011) Efficient subspace segmentation via quadratic programming. In: Twenty-Fifth AAAI Conference on artificial intelligence. August 2011, pp 519–524

  19. Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: Analysis and an algorithm. In: Advances in neural information processing systems, pp 849–856

  20. Gao H, Nie F, Li X, Huang H (2015) Multi-view subspace clustering. In: Proceedings of the IEEE international conference on computer vision, ICCV, vol 2015, pp 4238–4246

  21. Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR, pp 4279–4287

  22. Cao X, Zhang C, Fu H, Liu S, Zhang H (2015) Diversity-induced multi-view subspace clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR, pp 586–594

  23. Kumar A, Rai P, Daume H (2011) Co-regularized multi-view spectral clustering. Adv Neural Inf Process Syst 24:1413–1421

    Google Scholar 

  24. Brbić M, Kopriva I (2018) Multi-view low-rank sparse subspace clustering. Pattern Recogn 73:247–258

    Article  Google Scholar 

  25. Yin Q, Wu S, He R et al (2015) Multi-view clustering via pairwise sparse subspace representation. Neurocomputing 156:12–21

    Article  Google Scholar 

  26. Zhang C, Fu H, Liu S, Liu G, Cao X (2015) Low-rank tensor constrained multi-view subspace clustering. In: Proceedings of the IEEE international conference on computer vision, ICCV, pp 1582–1590

  27. Xu H, Zhang X, Xia W et al (2020) Low-rank tensor constrained co-regularized multi-view spectral clustering. Neural Netw 132:245–252

    Article  MATH  Google Scholar 

  28. Xie Y, Tao D, Zhang W et al (2018) On unifying multi-view self-representations for clustering by tensor multi-rank minimization. Int J Comput Vis 126(11):1157–1179

    Article  MathSciNet  MATH  Google Scholar 

  29. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  30. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  31. Yin M, Gao J, Lin Z (2015) Laplacian regularized low-rank representation and its applications. IEEE Trans Pattern Anal Mach Intell 38(3):504–517

    Article  Google Scholar 

  32. Cai D, He X, Han J et al (2010) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548–1560

    Google Scholar 

  33. Zhao W, Tan S, Guan Z et al (2018) Learning to map social network users by unified manifold alignment on hypergraph. IEEE Trans Neural Netw Learn Syst 29(12):5834–5846

    Article  MathSciNet  Google Scholar 

  34. Zhao W, Guan Z, Liu Z (2015) Ranking on heterogeneous manifolds for tag recommendation in social tagging services. Neurocomputing 148:521–534

    Article  Google Scholar 

  35. Zong L, Zhang X, Zhao L et al (2017) Multi-view clustering via multi-manifold regularized non-negative matrix factorization. Neural Netw 88:74–89

    Article  MATH  Google Scholar 

  36. Xu C, Guan Z, Zhao W, Niu Y, Wang Q, Wang Z (2018) Deep multi-view concept learning. In: IJCAI, pp 2898–2904

  37. Zhao W, Xu C, Guan Z et al (2020) Multiview concept learning via deep matrix factorization. IEEE Trans Neural Netw Learn Syst 32(2):814–825

    Article  MathSciNet  Google Scholar 

  38. Luo P, Peng J, Guan Z et al (2018) Dual regularized multi-view non-negative matrix factorization for clustering. Neurocomputing 294:1–11

    Article  Google Scholar 

  39. Hu Z, Nie F, Chang W et al (2020) Multi-view spectral clustering via sparse graph learning. Neurocomputing 384:1–10

    Article  Google Scholar 

  40. Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. Adv Neural Inf Process Syst 19:1601–1608

    Google Scholar 

  41. Liu J, Musialski P, Wonka P, Ye J (2009) Tensor completion for estimating missing values in visual data. In: IEEE International Conference on Computer Vision, ICCV, pp 2114–2121

  42. Liu J, Musialski P, Wonka P et al (2012) Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell 35(1):208–220

    Article  Google Scholar 

  43. Lin Z, Liu R, Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. Adv Neural Inf Process Syst 1:612–620

    Google Scholar 

  44. Lin Z, Chen M, Ma Y (2010) The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055

  45. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  46. Jolliffe IT (2002) Principal component analysis. J Mark Res 87(4):513

    MATH  Google Scholar 

  47. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791

    Article  MATH  Google Scholar 

  48. Lu GF, Yu QR, Wang Y et al (2020) Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint. Neural Netw 125:214–223

    Article  MATH  Google Scholar 

  49. Chen MS, Huang L, Wang CD et al (2021) Relaxed multi-view clustering in latent embedding space. Inf Fusion 68:8–21

    Article  Google Scholar 

  50. Wang H, Yang Y, Liu B (2019) GMC: graph-based multi-view clustering. IEEE Trans Knowl Data Eng 32(6):1116–1129

    Article  Google Scholar 

  51. Li Z, Hu Z, Nie F et al (2022) Multi-view clustering based on generalized low rank approximation. Neurocomputing 471:251–259

    Article  Google Scholar 

  52. Kang Z, Lin Z, Zhu X, Xu W (2021) Structured graph learning for scalable subspace clustering: from single view to multiview. IEEE Trans Cybern 52(9):8976–8986

    Article  Google Scholar 

  53. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  54. Lades M, Vorbruggen JC, Buhmann J et al (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300–311

    Article  Google Scholar 

  55. Han ZB, Zhang CQ, Fu HZ, Zhou JT (2022) Trusted multi-view classification with dynamic evidential fusion. In: IEEE transactions on pattern analysis and machine intelligence, pp 1–24

  56. Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579–2605

    MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grants 61806006, China Postdoctoral Science Foundation under Grant No. 2019M660149, the 111 Project under Grants No. B12018, and PAPD of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Guoqing Liu, Hongwei Ge, Ting Li & Shuangxi Wang

  2. Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, Wuxi, 214122, China

    Guoqing Liu, Hongwei Ge, Ting Li & Shuangxi Wang

  3. School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, 232001, Anhui, China

    Shuzhi Su

Authors
  1. Guoqing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongwei Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ting Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuzhi Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuangxi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Ge.

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

Liu, G., Ge, H., Li, T. et al. Multi-view subspace enhanced representation of manifold regularization and low-rank tensor constraint. Int. J. Mach. Learn. & Cyber. 14, 1811–1830 (2023). https://doi.org/10.1007/s13042-022-01729-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-022-01729-x

Keywords

Search

Navigation