Advertisement

Multi-View Data Completion

  • Sahely BhadraEmail author
Chapter
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Abstract

Multi-view learning has been explored in various applications such as bioinformatics, natural language processing and multimedia analysis. Often multi-view learning methods commonly assume that full feature matrices or kernel matrices for all views are available. However, in partial data analytics, it is common that information from some sources is not available or missing for some data-points. Such lack of information can be categorized into two types. (1) Incomplete view: information of a data-point is partially missing in some views. (2) Missing view: information of a data-point is entirely missing in some views, but information for that data-point is fully available in other views (no partially missing data-point in a view).

Although multi-view learning in the presence of missing data has drawn a great amount of attention in the recent past and there are quite a lot of research papers on multi-view data completion, but there is no comprehensive introduction and review of current approaches on multi-view data completion. We address this gap in this chapter through describing the multi-view data completion methods.

In this chapter, we will mainly discuss existing methods to deal with missing view problem. We describe a simple taxonomy of the current approaches. And for each category, representative as well as newly proposed models are presented. We also attempt to identify promising avenues and point out some specific challenges which can hopefully promote further research in this rapidly developing field.

References

  1. 1.
    Amini, M., Usunier, N., Goutte, C.: Learning from multiple partially observed views - an application to multilingual text categorization. In: Advances in Neural Information Processing Systems, vol. 22, pp. 28–36 (2009)Google Scholar
  2. 2.
    Argyriou, A., Micchelli, C.A., Pontil, M.: Learning convex combinations of continuously parameterized basic kernels. In: Proceedings of the 18th Annual Conference on Learning Theory, pp. 338–352 (2005)CrossRefGoogle Scholar
  3. 3.
    Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. In: Advances in Neural Information Processing Systems, pp. 41–48 (2006)Google Scholar
  4. 4.
    Ashraphijuo, M., Wang, X., Aggarwal, V.: A characterization of sampling patterns for low-rank multi-view data completion problem. In: 2017 IEEE International Symposium on Information Theory (ISIT), pp. 1147–1151. IEEE (2017)Google Scholar
  5. 5.
    Bach, F., Lanckriet, G., Jordan, M.: Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the 21st International Conference on Machine Learning, pp. 6–13. ACM, New York (2004)Google Scholar
  6. 6.
    Bhadra, S., Kaski, S., Rousu, J.: Multi-view kernel completion. Mach. Learn. 106(5), 713–739 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM, New York (1998)Google Scholar
  8. 8.
    Bunte, K., Leppäaho, E., Saarinen, I., Kaski, S.: Sparse group factor analysis for biclustering of multiple data sources. Bioinformatics 32(16), 2457–2463 (2016)CrossRefGoogle Scholar
  9. 9.
    Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chao, G., Sun, S.: Multi-kernel maximum entropy discrimination for multi-view learning. Intell. Data Anal. 20(3), 481–493 (2016)CrossRefGoogle Scholar
  11. 11.
    Christoudias, C., Urtasun, R., Darrell, T.: Multi-view learning in the presence of view disagreement (2012). Preprint. arXiv:1206.3242Google Scholar
  12. 12.
    Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res. 13, 795–828 (2012)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Cristianini, N., Shawe-Taylor, J., Lodhi, H.: Latent semantic kernels. J. Intell. Inf. Syst. 18(2-3), 127–152 (2002)CrossRefGoogle Scholar
  14. 14.
    Daemen, A., Griffith, O., Heiser, L., et al.: Modeling precision treatment of breast cancer. Genome Biol. 14(10), R110 (2013)CrossRefGoogle Scholar
  15. 15.
    Dhillon, P., Foster, D.P., Ungar, L.H.: Multi-view learning of word embeddings via CCA. In: Advances in Neural Information Processing Systems, pp. 199–207 (2011)Google Scholar
  16. 16.
    Fan, J., Chow, T.: Deep learning based matrix completion. Neurocomputing 266, 540–549 (2017)CrossRefGoogle Scholar
  17. 17.
    Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12(Jul), 2211–2268 (2011)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Heiser, L.M., Sadanandam, A., et al.: Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc. Natl. Acad. Sci. 109(8), 2724–2729 (2012)CrossRefGoogle Scholar
  19. 19.
    Kan, M., Shan, S., Zhang, H., Lao, S., Chen, X.: Multi-view discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 188–194 (2016)CrossRefGoogle Scholar
  20. 20.
    Klopp, O., Lounici, K., Tsybakov, A.B.: Robust matrix completion. Probab. Theory Relat. Fields 169(1–2), 523–564 (2017)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1413–1421 (2011)Google Scholar
  22. 22.
    Li, Y., Wu, F.X., Ngom, A.: A review on machine learning principles for multi-view biological data integration. Brief. Bioinform. (2018).  https://doi.org/10.1093/bib/bbw113
  23. 23.
    Lian, W., Rai, P., Salazar, E., Carin, L.: Integrating features and similarities: Flexible models for heterogeneous multiview data. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2757–2763 (2015)Google Scholar
  24. 24.
    Livescu, K., Stoehr, M.: Multi-view learning of acoustic features for speaker recognition. In: IEEE Workshop on Automatic Speech Recognition & Understanding, ASRU 2009, pp. 82–86. IEEE (2009)Google Scholar
  25. 25.
    Müller, K., Schwarz, H., Marpe, D., Bartnik, C., Bosse, S., Brust, H., Hinz, T., Lakshman, H., Merkle, P., Rhee, F.H., et al.: 3d high-efficiency video coding for multi-view video and depth data. IEEE Trans. Image Process. 22(9), 3366–3378 (2013)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Schafer, J.L., Graham, J.W.: Missing data: our view of the state of the art. Psychol. Methods 7(2), 147 (2002)CrossRefGoogle Scholar
  27. 27.
    Shao, W., Shi, X., Yu, P.S.: Clustering on multiple incomplete datasets via collective kernel learning. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1181–1186. IEEE (2013)Google Scholar
  28. 28.
    Subramanya, S., Li, B., Liu, H.: Robust integration of multiple information sources by view completion. In: IEEE International Conference on Information Reuse and Integration, IRI 2008, pp. 398–403. IEEE (2008)Google Scholar
  29. 29.
    Subramanya, S., Wang, Z., Li, B., Liu, H.: Completing missing views for multiple sources of web media. Int. J. Data Min. Model. Manag. 1(1), 23–44 (2008)zbMATHGoogle Scholar
  30. 30.
    Trivedi, A., Rai, P., Daumé III, H., DuVall, S.L.: Multiview clustering with incomplete views. In: Proceedings of the NIPS Workshop (2005)Google Scholar
  31. 31.
    Tsuda, K., Akaho, S., Asai, K.: The em algorithm for kernel matrix completion with auxiliary data. J. Mach. Lear. Res. 4, 67–81 (2003)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Virtanen, S., Klami, A., Khan, S., Kaski, S.: Bayesian group factor analysis. In: Artificial Intelligence and Statistics, pp. 1269–1277 (2012)Google Scholar
  33. 33.
    Wan, X.: Co-training for cross-lingual sentiment classification. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1, pp. 235–243. Association for Computational Linguistics (2009)Google Scholar
  34. 34.
    Williams, D., Carin, L.: Analytical kernel matrix completion with incomplete multi-view data. In: Proceedings of the ICML Workshop on Learning With Multiple Views (2005)Google Scholar
  35. 35.
    Williams, C., Seeger, M.: Using the nyström method to speed up kernel machines. In: Proceedings of the 14th Annual Conference on Neural Information Processing Systems, pp. 682–688. No. EPFL-CONF-161322 (2001)Google Scholar
  36. 36.
    Xu, C., Tao, D., Xu, C.: A survey on multi-view learning (2013). Preprint. arXiv:1304.5634Google Scholar
  37. 37.
    Xu, J., Han, J., Nie, F.: Discriminatively embedded k-means for multi-view clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5356–5364 (2016)Google Scholar
  38. 38.
    Xue, Z., Li, G., Huang, Q.: Joint multi-view representation learning and image tagging. In AAAI, pp. 1366–1372 (2016)Google Scholar
  39. 39.
    Zhao, J., Xie, X., Xu, X., Sun, S.: Multi-view learning overview: recent progress and new challenges. Inf. Fusion 38, 43–54 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of TechnologyPalakkadIndia

Personalised recommendations