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Multi-view Similarity Learning of Manifold Data

  • Rui-rui Wang
  • Si-bao ChenEmail author
  • Bin Luo
  • Jian Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)

Abstract

In recent years, multi-view learning methods have developed rapidly where graph-based approaches have achieved good performance. Usually, these learning methods construct information graph for each view or fuse different views into one graph. In this paper, a novel multi-view learning model that learns one similarity matrix for all views named Multi-view Similarity Learning (MSL) is proposed, where adaptive weights are learned for each view. The multi-view similarity learning method is further extended to kernel space. Experiments of classification, clustering and semi-supervised classification on different real-world datasets show the effectiveness of the proposed method.

Keywords

Laplacian Eigenmaps Multi-view learning Similarity learning 

References

  1. 1.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003)CrossRefGoogle Scholar
  2. 2.
    Chen, S., Ding, C.H.Q., Luo, B.: Similarity learning of manifold data. IEEE Trans. Cybern. 45(9), 1744–1756 (2015)CrossRefGoogle Scholar
  3. 3.
    Chik, Z., Aljanabi, Q.A., Kasa, A., Taha, M.R.: Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment. Arab. J. Geosci. 7(11), 4877–4887 (2014)CrossRefGoogle Scholar
  4. 4.
    Cox, M.A.A., Cox, T.F.: Multidimensional scaling. J. R. Stat. Soc. 46(2), 1050–1057 (2001)zbMATHGoogle Scholar
  5. 5.
    Debruyne, M., Verdonck, T.: Robust kernel principal component analysis and classification. Adv. Data Anal. Classif. 4(2–3), 151–167 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Bus. Econ. Stat. 13(1), 134–144 (1995)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-39964-3_62CrossRefGoogle Scholar
  8. 8.
    Hsieh, P., Yang, M., Gu, Y., Liang, Y.: Classification-oriented locally linear embedding. IJPRAI 24(5), 737–762 (2010)Google Scholar
  9. 9.
    Kumar, A., III, H.D.: A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, 28 June–2 July 2011, pp. 393–400 (2011)Google Scholar
  10. 10.
    Liu, C., Wechsler, H.: Independent component analysis of gabor features for face recognition. IEEE Trans. Neural Netw. 14(4), 919–928 (2003)CrossRefGoogle Scholar
  11. 11.
    Nie, F., Cai, G., Li, X.: Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 2408–2414 (2017)Google Scholar
  12. 12.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  13. 13.
    Wang, X., Tang, X.: Dual-space linear discriminant analysis for face recognition. In: 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 564–569 (2004)Google Scholar
  14. 14.
    Yang, M.-H.: Discriminant isometric mapping for face recognition. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 470–480. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-36592-3_45CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rui-rui Wang
    • 1
  • Si-bao Chen
    • 1
    • 2
    Email author
  • Bin Luo
    • 1
  • Jian Zhang
    • 2
  1. 1.Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.Peking University Shenzhen InstituteShenzhenChina

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