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A Framework for Multi-view Feature Selection via Embedding Space

  • Junhao Zhang
  • Yuan Wan
  • Yuting Pan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

Multi-view learning has drawn much attention in the past years to reveal the correlated and complemental information between different views. Feature selection for multi-view data is still a challenge in dimension reduction. Most of the multi-view feature selection methods simply concatenate all views together without capturing the information between different views. In this paper, we propose an embedding framework for multi-view feature selection, Embedding Space based Multi-view Feature Selection (ESMFS). ESMFS comes up with a new concept called mapping consensus to embed all views of data to a unified space. By preserving the manifold information, ESMFS captures the fusing views’ information. ESMFS is suitable for both supervised and unsupervised feature selection. For practical purpose, we propose two methods ES-LRFS and ES-MAFS to illustrate ESMFS framework. Experiments show that ES-LRFS and ES-MAFS are of inclusiveness and efficiency for multi-view feature selection, thus proving the feasibility of ESMFS.

Keywords

Multi-view Feature selection Feature embedding 

Notes

Acknowledgment

This research is supported by the National Natural Science Foundation of China (No. 61573012).

References

  1. 1.
    Long, B., Yu, P.S., Zhang, Z.: A general model for multiple view unsupervised learning. In: SIAM International Conference on Data Mining, SDM 2008, Atlanta, Georgia, USA, 24–26 April 2008, pp. 822–833 (2013)Google Scholar
  2. 2.
    Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B 40(6), 1438–1446 (2010)CrossRefGoogle Scholar
  3. 3.
    Zhang, L., Zhang, Q., Zhang, L., Tao, D., Huang, X., Du, B.: Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recognit. 48(10), 3102–3112 (2015)CrossRefGoogle Scholar
  4. 4.
    Li, J., Wu, Y., Zhao, J., Lu, K.: Low-rank discriminant embedding for multiview learning. IEEE Trans. Cybern. 47(11), 3516 (2017)CrossRefGoogle Scholar
  5. 5.
    Wan, Y., Chen, X., Zhang, J.: Global and intrinsic geometric structure embedding for unsupervised feature selection. Expert Syst. Appl. (2017)Google Scholar
  6. 6.
    Wei, X., Cao, B., Yu, P.S.: Multi-view unsupervised feature selection by cross-diffused matrix alignment. In: International Joint Conference on Neural Networks (2017)Google Scholar
  7. 7.
    Feng, Y., Xiao, J., Zhuang, Y., Liu, X.: Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 343–357. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37331-2_26CrossRefGoogle Scholar
  8. 8.
    Tang, J., Hu, X., Gao, H., Liu, H.: Unsupervised feature selection for multi-view data in social media (2013)Google Scholar
  9. 9.
    Qian, M., Zhai, C.: Unsupervised feature selection for multi-view clustering on text-image web news data. In: ACM International Conference on Conference on Information and Knowledge Management, pp. 1963–1966 (2014)Google Scholar
  10. 10.
    Zhang, T., Tao, D., Li, X., Yang, J.: Patch alignment for dimensionality reduction. IEEE Trans. Knowl. Data Eng. 21(9), 1299–1313 (2009)CrossRefGoogle Scholar
  11. 11.
    Du, L., Shen, Y.D.: Unsupervised feature selection with adaptive structure learning, vol. 37, no. 7, pp. 209–218 (2015)Google Scholar
  12. 12.
    Li, Z., Yang, Y., Liu, J., Zhou, X., Lu, H.: Unsupervised feature selection using nonnegative spectral analysis. In: Open image in new window, vol. 2, pp. 1026–1032 (2012)Google Scholar
  13. 13.
    Wei, X., Cao, B., Yu, P.S.: Nonlinear joint unsupervised feature selection. In: SIAM International Conference on Data Mining, pp. 414–422 (2016)Google Scholar
  14. 14.
    He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: International Conference on Neural Information Processing Systems, pp. 507–514 (2006)Google Scholar
  15. 15.
    Kumar, A., Rai, P.: Co-regularized multi-view spectral clustering. In: International Conference on Neural Information Processing Systems, pp. 1413–1421 (2011)Google Scholar
  16. 16.
    Qian, M., Zhai, C.: Robust unsupervised feature selection. In: International Joint Conference on Artificial Intelligence, pp. 1621–1627 (2013)Google Scholar
  17. 17.
    Chen, X., Zhou, G., Chen, Y., Shao, G., Gu, Y.: Supervised multiview feature selection exploring homogeneity and heterogeneity with \(l_{1,2}\)-norm and automatic view generation. IEEE Trans. Geosci. Remote Sens. PP(99), 1–15 (2017)Google Scholar
  18. 18.
    Chen, X., Liu, W., Su, F., Zhou, G.: Semisupervised multiview feature selection for VHR remote sensing images with label learning and automatic view generation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. PP(99), 1–13 (2017)Google Scholar
  19. 19.
    Chen, X., Liu, W., Su, F., Shao, G.: Semi-supervised multiview feature selection with label learning for VHR remote sensing images. In: Geoscience and Remote Sensing Symposium, pp. 2372–2375 (2016)Google Scholar
  20. 20.
    Chen, X., Song, L., Hou, Y., Shao, G.: Efficient semi-supervised feature selection for VHR remote sensing images. In: Geoscience and Remote Sensing Symposium, pp. 1500–1503 (2016)Google Scholar
  21. 21.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323 (2000)CrossRefGoogle Scholar
  22. 22.
    Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  23. 23.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, vol. 14, no. 6 (2001)Google Scholar
  24. 24.
    Donoho, D.L., Grimes, C.: Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. U.S.A. 100(10), 5591 (2003)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Zhang, Z.Y., Zha, H.Y.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Adv. Manuf. Open image in new window) 8(4), 406–424 (2004)Google Scholar
  26. 26.
    Wang, T., Zhao, D., Tian, S.: An overview of kernel alignment and its applications. Artif. Intell. Rev. 43(2), 179–192 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of MathematicsWuhan University of TechnologyWuhanChina
  2. 2.School of LawGuangxi UniversityNanningChina

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