Skip to main content
SpringerLink
Log in

Semi-supervised one-pass multi-view learning

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Multi-view learning machines aim to process multi-view data set which consists of instances with different views. In recent years, the scale of multi-view data set is more and more larger and traditional multi-view learning machines which should store the entire data set cannot process them with the limitation of computation and storage ability. In order to overcome such a disadvantage, one-pass multi-view (OPMV) framework has been proposed and it goes through instances only once without storing the entire data set. But most multi-view data sets are semi-supervised, i.e., some training instances of the data sets are labeled while others are unlabeled. Furthermore, for a real-world multi-view data set, labeling instances is a high-cost task. In this paper, we propose semi-supervised OPMV (SSOPMV) framework for solving this issue. In SSOPMV, we develop an approach to generate additional unlabeled instances which possess useful discriminant information and apply them into the original OPMV framework along with the labeled and original unlabeled training instances. We verify the effectiveness of the SSOPMV with some real-world semi-supervised multi-view data sets used theoretically and empirically.

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
Fig. 8

Similar content being viewed by others

References

  1. Ye HJ, Zhan DC, Miao Y, Jiang Y, Zhou ZH (2015) Rank consistency based multi-view learning: a privacy-preserving approach. In: ACM international on conference on information and knowledge management, pp. 991–1000

  2. Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664

    Article  Google Scholar 

  3. Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. IEEE Confer Comput Vis Pattern Recogn 157:2160–2167

    Google Scholar 

  4. Gönen M, Alpaydin E (2011) Multiple Kernel learning algorithms. J Mach Learn Res 12:2211–2268

    MathSciNet  MATH  Google Scholar 

  5. Ye G, Liu D, Jhuo IH, Huan J (2012) Robust late fusion with rank minimization. Comput Vis Pattern Recogn 112:3021–3028

    Google Scholar 

  6. Fang YX, Zhang HJ, Ye YM, Li XT (2014) Detecting hot topics from Twitter: a multiview approach. J Inf Sci 40(5):578–593

    Article  Google Scholar 

  7. Zhang HJ, Liu G, Chow TWS, Liu WY (2011) Textual and visual content-based anti-phishing: a bayesian approach. IEEE Trans Neural Netw 22(10):1532–1546

    Article  Google Scholar 

  8. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: 11th conference on computational learning theory, pp. 92–100

  9. Wang W, Zhou ZH (2010) Multi-view active learning in the non-realizable case. Adv Neural Inf Process Syst 23:2388–2396

    MathSciNet  Google Scholar 

  10. Zhou ZH, Li M (2007) Semi-supervised learning with very few labeled training examples. In: Proceeding of the 22nd AAAI conference on artificial intelligence, pp. 675–680

  11. Zhu Y, Gao W, Zhou ZH (2015) One-pass multi-view learning. J Mach Learn Res 30:1–16

    Google Scholar 

  12. Vapnik V, Kotz S (1982) Estimation of dependences based on empirical data. Springer, New York

    MATH  Google Scholar 

  13. Zhu CM (2016) Improved multi-kernel classification machine with Nyström approximation technique and Universum data. Neurocomputing 175:610–634

    Article  Google Scholar 

  14. Zhu CM (2017) Double-fold localized multiple matrix learning machine with Universum. Pattern Anal Appl 20(4):1091–1118

    Article  MathSciNet  Google Scholar 

  15. Chen XH, Chen SC, Xue H (2012) Universum linear discriminant analysis. Electron Lett 48(22):1407–1409

    Article  Google Scholar 

  16. Wang Z, Zhu YJ, Liu WW, Chen ZH, Gao DQ (2014) Multi-view learning with Universum. Knowl Based Syst 70(C):376–391

    Article  Google Scholar 

  17. Tencer L, Reznakova M, Cheriet M (2017) UFuzzy: fuzzy models with Universum. Appl Soft Comput 52:1296–1315

    Article  Google Scholar 

  18. Liu CL, Hsaio WH, Lee CH, Chang TH, Kuo TH (2017) Semi-supervised text classification with universum learning. IEEE Trans Cybern 46(2):462–473

    Article  Google Scholar 

  19. Deng J, Xu X, Zhang Z, Fruhholz S, Schuller B (2017) Universum autoencoder-based domain adaptation for speech emotion recognition. IEEE Signal Process Lett 24(4):500–504

    Article  Google Scholar 

  20. Chen S, Zhang CS (2009) Selecting informative Universum sample for semi-supervised learning. In: International joint conference on artificial intelligence, pp. 1016–1021

  21. Zhang D, Wang J, Si L (2011) Document clustering with universum, International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 873-882

  22. Shen C, Wang P, Shen F, Wang H (2012) Uboost: boosting with the universum. IEEE Trans Pattern Anal Mach Intell 34(4):825

    Article  Google Scholar 

  23. He J, Du CY, Zhuang FZ, Yin X, He Q, Long GP (2016) Online Bayesian max-margin subspace multi-view learning. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp. 1555–1561

  24. Hou CP, Zhou ZH (2017) One-pass learning with incremental and decremental features. IEEE Trans Pattern Anal Mach Intell 35:171. https://doi.org/10.1109/TPAMI.2017.2769047

    Article  Google Scholar 

  25. Tao Y, Triebel R, Cremers D (2015) Semi-supervised online learning for efficient classification of objects in 3D data streams. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 2904–2910

  26. Kunwar R, Pal U, Blumenstein M (2013) Semi-supervised online learning of handwritten characters using a Bayesian classifier. In: 2013 second IAPR Asian conference on pattern recognition, pp. 717–721

  27. Zhang GX, Jiang ZL, Davis LS (2012) Online semi-supervised discriminative dictionary learning for sparse representation. In: Asian conference on computer vision, pp. 259–273

  28. Xu YM, Wang CD, Lai JH (2016) Weighted multi-view clustering with feature selection. Pattern Recogn 53:25–35

    Article  Google Scholar 

  29. Pearson K (1905) The problem of the random walk. Nature 72(1865):294

    Article  Google Scholar 

  30. Zhu XJ, Ghahramani ZB, Lafferty J (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the twentieth international conference on machine learning, pp. 912–919

  31. Zhou DY, Bousquet O, Lal TN, Weston J, Schölkopf B (2003) Learning with local and global consistency. Int Confer Neural Inf Process Syst 16(4):321–328

    Google Scholar 

  32. https://en.wikipedia.org/wiki/Random_walker_algorithm. Accessed 10 July 2018

  33. McCallum A, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retr 3(2):127–163

    Article  Google Scholar 

  34. Bisson G, Grimal C (2012) Co-clustering of multi-view datasets: a parallelizable approach. In: Proceedings of the 12th IEEE international conference on data mining (ICDM’12), pp. 828–833

  35. Hussain S, Grimal C, Bisson G (2010) An improved co-similarity measure for document clustering. In: Proceedings of 9th international conference on machine learning and applications (ICMLA’ 10), pp. 190–197

  36. Amini M, Usunier N, Goutte C (2009) Learning from multiple partially observed views—an application to multilingual text categorization. Adv Neural Inf Process Syst 22:28–36

    Google Scholar 

  37. Zhu SH, Sun X, Jin DL (2016) Multi-view semi-supervised learning for image classification. Neurocomputing 208:136–142

    Article  Google Scholar 

  38. Sun SL, Zhang QJ (2011) Multiple-view multiple-learner semi-supervised learning. Neural Process Lett 34:229–240

    Article  Google Scholar 

  39. Du YT, Li Q, Cai ZM, Guan XH (2013) Multi-view semi-supervised web image classification via co-graph. Neurocomputing 122:430–440

    Article  Google Scholar 

  40. Gu P, Zhu QS, Zhang C (2009) A multi-view approach to semi-supervised document classification with incremental Naive Bayes. Comput Math Appl 57(6):1030–1036

    Article  Google Scholar 

  41. Yang ZK, Liu Z, Liu SY, Min L, Meng WT (2014) Adaptive multi-view selection for semi-supervised emotionrecognition of posts in online student community. Neurocomputing 144:138–150

    Article  Google Scholar 

  42. Bach R, Lanckriet GR, Jordan MI (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the 21st international conference on machine learning, pp. 6–13

  43. Ye G, Liu D, Jhuo IH, Chang SF (2012) Robust late fusion with rank minimization. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp. 3021–3028

  44. Cortes C, Mohri M, Rostamizadeh A (2010) Two-stage learning kernel algorithms. In: Proceedings of the 27th international conference on machine learning, pp. 239–246

  45. Rakotomamonjy A, Bach F, Canu S, Grandvalet Y (2008) SimpleMKL. J Mach Learn Res 9:2491–2521

    MathSciNet  MATH  Google Scholar 

  46. Kloft M, Brefeld U, Sonnenburg S, Zien A (2010) Non-sparse regularization and efficient training with multiple kernels. arxiv preprint arXiv:1003.0079

  47. Gönen M, Alpaydin E (2008) Localized multiple kernel learning. In: Proceeding of the 25th international conference on machine learning, pp. 352–359

  48. Iosifidis A, Tefas A, Nikolaidis N, Pitas I (2012) Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis. Comput Vis Image Underst 116(3):347–360

    Article  Google Scholar 

  49. Rupnik J, Shawe-Taylor J (2010) Multi-view canonical correlation analysis. In: Proceeding of Slovenian KDD conference on data mining data warehouses, pp. 1–4

  50. Yin X, Huang Q, Chen X (2011) Multiple view locality preserving projections with pairwise constraints. Commun Syst Inf Technol 100:859–866

    Article  Google Scholar 

  51. Weston J, Collobert R, Sinz F, Bottou L, Vapnik V (2006) Inference with the universum. In: The 23rd international conference on machine learning, pp. 1009–1016

  52. Sinz F, Chapelle O, Agarwal A, Schölkopf B (2008) An analysis of inference with the universum. In: Advances in neural information processing systems (NIPS 2008), pp. 1369–1376

Download references

Acknowledgements

This work is supported by (1) Natural Science Foundation of Shanghai under Grant Nos. 16ZR1414500 and 16ZR1414400 (2) National Natural Science Foundation of China under Grant Nos. 61602296, 51575336, 61603245, and 41701523 (3) PuJiang talent plan under Grant No. 16PJ1403700 and the authors would like to thank their supports.

Author information

Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Changming Zhu, Rigui Zhou, Lai Wei & Xiafen Zhang

  2. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Zhe Wang

  3. Logistics Research Center, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Yi Ding

Authors
  1. Changming Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhe Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rigui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lai Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiafen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yi Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changming Zhu.

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, C., Wang, Z., Zhou, R. et al. Semi-supervised one-pass multi-view learning. Neural Comput & Applic 31, 8117–8134 (2019). https://doi.org/10.1007/s00521-018-3654-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3654-3

Keywords

Search

Navigation