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Multi-View Data Completion

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Linking and Mining Heterogeneous and Multi-view Data

Part of the book series: Unsupervised and Semi-Supervised Learning ((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.

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References

  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. 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)

    Chapter  Google Scholar 

  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. 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. 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. Bhadra, S., Kaski, S., Rousu, J.: Multi-view kernel completion. Mach. Learn. 106(5), 713–739 (2017)

    Article  MathSciNet  Google Scholar 

  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. Bunte, K., Leppäaho, E., Saarinen, I., Kaski, S.: Sparse group factor analysis for biclustering of multiple data sources. Bioinformatics 32(16), 2457–2463 (2016)

    Article  Google Scholar 

  9. Candès, E.J., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717 (2009)

    Article  MathSciNet  Google Scholar 

  10. Chao, G., Sun, S.: Multi-kernel maximum entropy discrimination for multi-view learning. Intell. Data Anal. 20(3), 481–493 (2016)

    Article  Google Scholar 

  11. Christoudias, C., Urtasun, R., Darrell, T.: Multi-view learning in the presence of view disagreement (2012). Preprint. arXiv:1206.3242

    Google Scholar 

  12. Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res. 13, 795–828 (2012)

    MathSciNet  MATH  Google Scholar 

  13. Cristianini, N., Shawe-Taylor, J., Lodhi, H.: Latent semantic kernels. J. Intell. Inf. Syst. 18(2-3), 127–152 (2002)

    Article  Google Scholar 

  14. Daemen, A., Griffith, O., Heiser, L., et al.: Modeling precision treatment of breast cancer. Genome Biol. 14(10), R110 (2013)

    Article  Google Scholar 

  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. Fan, J., Chow, T.: Deep learning based matrix completion. Neurocomputing 266, 540–549 (2017)

    Article  Google Scholar 

  17. Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12(Jul), 2211–2268 (2011)

    MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Klopp, O., Lounici, K., Tsybakov, A.B.: Robust matrix completion. Probab. Theory Relat. Fields 169(1–2), 523–564 (2017)

    Article  MathSciNet  Google Scholar 

  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. 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. 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. 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. 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)

    Article  MathSciNet  Google Scholar 

  26. Schafer, J.L., Graham, J.W.: Missing data: our view of the state of the art. Psychol. Methods 7(2), 147 (2002)

    Article  Google Scholar 

  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. 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. 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)

    MATH  Google Scholar 

  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. Tsuda, K., Akaho, S., Asai, K.: The em algorithm for kernel matrix completion with auxiliary data. J. Mach. Lear. Res. 4, 67–81 (2003)

    MathSciNet  MATH  Google Scholar 

  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. 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. 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. 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. Xu, C., Tao, D., Xu, C.: A survey on multi-view learning (2013). Preprint. arXiv:1304.5634

    Google Scholar 

  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. Xue, Z., Li, G., Huang, Q.: Joint multi-view representation learning and image tagging. In AAAI, pp. 1366–1372 (2016)

    Google Scholar 

  39. Zhao, J., Xie, X., Xu, X., Sun, S.: Multi-view learning overview: recent progress and new challenges. Inf. Fusion 38, 43–54 (2017)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Indian Institute of Technology, Palakkad, India

    Sahely Bhadra

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  1. Sahely Bhadra
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Correspondence to Sahely Bhadra .

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Editors and Affiliations

  1. Queen’s University Belfast, Northern Ireland, UK

    Deepak P

  2. Queen’s University Belfast, Northern Ireland, UK

    Anna Jurek-Loughrey

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Bhadra, S. (2019). Multi-View Data Completion. In: P, D., Jurek-Loughrey, A. (eds) Linking and Mining Heterogeneous and Multi-view Data. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-01872-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-01872-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01871-9

  • Online ISBN: 978-3-030-01872-6

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