Advertisement

Active Multivariate Matrix Completion

  • Tianshi LiuEmail author
  • Qiong Wu
  • Wenjuan Zhang
  • Xinran Cao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 950)

Abstract

Matrix completion has been rapidly grown interested in areas of engineering and applied science. however, most of applications are aim at single variate matrix. In fact, majority applications datasets express in form of multivariate matrix, developing multivariate matrix application is very necessary. The important is when the missing values too many, matrix completion of take advantage of active learning performance better than standard matrix completion. Although, several combining active learning for matrix completion solutions have been proposed, and most of them based on query strategy. But none of them emphasize the important location of matrix for recovering a matrix. In this paper, we design active multivariate matrix completion. The goal of this algorithm is that find a important location of matrix for matrix completion and combine rank aggregation to select query entries. Experiment evaluation base on images datasets. when we query a small amount of missing entries, the proposed active multivariate matrix completion efficiently raise the accuracy of matrix completion and give the important position of missing entries.

Keywords

Matrix completion Active learning Rank aggregation 

References

  1. 1.
    Yick, J., Mukherjee, B., Ghosal, D.: Comput. Netw. 52(12), 2292 (2008).  https://doi.org/10.1016/j.comnet.2008.04.002CrossRefGoogle Scholar
  2. 2.
    Candès, E.J., Tao, T.: IEEE Trans. Inf. Theory 56(5), 2053 (2010).  https://doi.org/10.1109/TIT.2010.2044061MathSciNetCrossRefGoogle Scholar
  3. 3.
    Yuan, M., Lin, Y.: Biometrika 94(1), 19 (2007).  https://doi.org/10.1093/biomet/asm018MathSciNetCrossRefGoogle Scholar
  4. 4.
    Candès, E.J., Recht, B.: Commun. ACM 55, 111 (2012)CrossRefGoogle Scholar
  5. 5.
    Kang, Z., Peng, C., Cheng, Q.: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 12–17 February 2016, Phoenix, Arizona, USA, pp. 179–185 (2016)Google Scholar
  6. 6.
    Zhang, X., Yin, C.: 9th International Conference on Wireless Communications and Signal Processing, WCSP 2017, 11–13 October 2017, Nanjing, China, pp. 1–5 (2017)Google Scholar
  7. 7.
    Krishnamurthy, A., Singh, A.: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held 5–8 December 2013, Lake Tahoe, Nevada, United States, pp. 836–844 (2013). http://papers.nips.cc/paper/4954-low-rank-matrix-and-tensor-completion-via-adaptive-sampling
  8. 8.
    Silva, J.G., Carin, L.: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, 12–16 August 2012, Beijing, China, pp. 325–333 (2012).  https://doi.org/10.1145/2339530.2339584
  9. 9.
    Wauthier, F.L., Jojic, N., Jordan, M.I.: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, 12–16 August 2012, Beijing, China, pp. 1339–1347 (2012).  https://doi.org/10.1145/2339530.2339737
  10. 10.
    Raymond, R., Kashima, H.: Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, 20–24 September 2010, Barcelona, Spain, Proceedings Part III, pp. 131–147 (2010).  https://doi.org/10.1007/978-3-642-15939-8_9CrossRefGoogle Scholar
  11. 11.
    Chakraborty, S., Zhou, J., Balasubramanian, V.N., Panchanathan, S., Davidson, I., Ye, J.: 2013 IEEE 13th International Conference on Data Mining, 7–10 December 2013, Dallas, TX, USA, pp. 81–90 (2013)Google Scholar
  12. 12.
    Ruchansky, N., Crovella, M., Terzi, E.: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 10–13 August 2015, Sydney, NSW, Australia, pp. 1025–1034 (2015).  https://doi.org/10.1145/2783258.2783259
  13. 13.
    Mavroforakis, C., Erdös, D., Crovella, M., Terzi, E.: Proceedings of the 2017 SIAM International Conference on Data Mining, 27–29 April 2017, Houston, Texas, USA, pp. 264–272 (2017).  https://doi.org/10.1137/1.9781611974973.30CrossRefGoogle Scholar
  14. 14.
    Städler, N., Bühlmann, P.: Stat. Comput. 22, 219 (2012).  https://doi.org/10.1007/s11222-010-9219-7MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mazumder, R., Hastie, T.: J. Mach. Learn. Res. 13, 781 (2012). http://dl.acm.org/citation.cfm?id=2188412

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Tianshi Liu
    • 1
    Email author
  • Qiong Wu
    • 1
  • Wenjuan Zhang
    • 1
  • Xinran Cao
    • 1
  1. 1.Xi’an Shiyou UniversityXi’anChina

Personalised recommendations