Skip to main content
SpringerLink
Log in

Adaptive Graph Fusion for Unsupervised Feature Selection

  • Conference paper
  • First Online:
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11728))

Included in the following conference series:

  • 4087 Accesses

Abstract

The massive high-dimensional data brings about great time complexity, high storage burden and poor generalization ability of learning models. Feature selection can alleviate curse of dimensionality by selecting a subset of features. Unsupervised feature selection is much challenging due to lack of label information. Most methods rely on spectral clustering to generate pseudo labels to guide feature selection in unsupervised setting. Graphs for spectral clustering can be constructed in different ways, e.g., kernel similarity, or self-representation. The construction of adjacency graphs could be affected by the parameters of kernel functions, the number of nearest neighbors or the size of the neighborhood. However, it is difficult to evaluate the effectiveness of different graphs in unsupervised feature selection. Most existing algorithms only select one graph by experience. In this paper, we propose a novel adaptive multi-graph fusion based unsupervised feature selection model (GFFS). The proposed model is free of graph selection and can combine the complementary information of different graphs. Experiments on benchmark datasets show that GFFS outperforms the state-of-the-art unsupervised feature selection algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://featureselection.asu.edu/datasets.php.

  2. 2.

    http://www-i6.informatik.rwth-aachen.de/.

  3. 3.

    https://sites.google.com/site/feipingnie/.

References

  1. Benabdeslem, K., Hindawi, M.: Efficient semi-supervised feature selection: constraint, relevance, and redundancy. IEEE Trans. Knowl. Data Eng. 26(5), 1131–1143 (2014). https://doi.org/10.1109/TKDE.2013.86

    Article  Google Scholar 

  2. Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng. 17(12), 1624–1637 (2005). https://doi.org/10.1109/TKDE.2005.198

    Article  Google Scholar 

  3. Cai, D., Zhang, C., He, X.: Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 333–342. ACM (2010). https://doi.org/10.1145/1835804.1835848

  4. Dy, J.G., Brodley, C.E., Kak, A., Broderick, L.S., Aisen, A.M.: Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Trans. Pattern Anal. Mach. Intell. 25(3), 373–378 (2003). https://doi.org/10.1109/TPAMI.2003.1182100

    Article  Google Scholar 

  5. Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2790–2797. IEEE (2009). https://doi.org/10.1109/CVPRW.2009.5206547

  6. Fang, Y., Wang, R., Dai, B.: Graph-oriented learning via automatic group sparsity for data analysis. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 251–259. IEEE (2012). https://doi.org/10.1109/ICDM.2012.82

  7. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  8. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, pp. 507–514 (2005)

    Google Scholar 

  9. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005). https://doi.org/10.1109/TPAMI.2005.55

    Article  Google Scholar 

  10. Hou, C., Nie, F., Yi, D., Wu, Y.: Feature selection via joint embedding learning and sparse regression. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, pp. 1324–1329 (2011). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-224

  11. Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of the 2nd Berkeley Symposium on Mathematical Statistics and Probability, pp. 481–492 (1951)

    Google Scholar 

  12. Li, Z., Yang, Y., Liu, J., Zhou, X., Lu, H., et al.: Unsupervised feature selection using nonnegative spectral analysis. In: AAAI, vol. 2, pp. 1026–1032 (2012)

    Google Scholar 

  13. Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013). https://doi.org/10.1109/TPAMI.2012.88

    Article  Google Scholar 

  14. Liu, Y., Jin, R., Yang, L.: Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: AAAI, vol. 6, pp. 421–426 (2006)

    Google Scholar 

  15. Liu, Y., Zhang, C., Zhu, P., Hu, Q.: Generalized multi-view unsupervised feature selection. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11140, pp. 469–478. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01421-6_45

    Chapter  Google Scholar 

  16. Lu, C.-Y., Min, H., Zhao, Z.-Q., Zhu, L., Huang, D.-S., Yan, S.: Robust and efficient subspace segmentation via least squares regression. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 347–360. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33786-4_26

    Chapter  Google Scholar 

  17. Lu, C., Tang, J., Lin, M., Lin, L., Yan, S., Lin, Z.: Correntropy induced L2 graph for robust subspace clustering. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1801–1808 (2013). https://doi.org/10.1109/ICCV.2013.226.

  18. Luo, D., Nie, F., Ding, C., Huang, H.: Multi-subspace representation and discovery. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 405–420. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23783-6_26

    Chapter  Google Scholar 

  19. 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, 4–9 February 2017, San Francisco, California, USA, pp. 2408–2414 (2017). http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14833

  20. Nie, F., Huang, H., Cai, X., Ding, C.H.: Efficient and robust feature selection via joint l2, 1-norms minimization. In: Advances in Neural Information Processing Systems, pp. 1813–1821 (2010)

    Google Scholar 

  21. Nie, F., Li, J., Li, X., et al.: Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: IJCAI, pp. 1881–1887 (2016)

    Google Scholar 

  22. Qian, M., Zhai, C.: Robust unsupervised feature selection. In: IJCAI, pp. 1621–1627 (2013)

    Google Scholar 

  23. Tang, J., Liu, H.: Unsupervised feature selection for linked social media data. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 904–912. ACM (2012). https://doi.org/10.1145/2339530.2339673

  24. Wang, S., Tang, J., Liu, H.: Embedded unsupervised feature selection. In: AAAI, pp. 470–476 (2015)

    Google Scholar 

  25. Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 267–273. ACM (2003). https://doi.org/10.1145/860435.860485

  26. Yang, Y., Shen, H.T., Ma, Z., Huang, Z., Zhou, X.: L2, 1-norm regularized discriminative feature selection for unsupervised learning. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p. 1589 (2011). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-267.

  27. Zhao, Z., Liu, H.: Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 1151–1157. ACM (2007). https://doi.org/10.1145/1273496.1273641

  28. Zhao, Z., Wang, L., Liu, H., et al.: Efficient spectral feature selection with minimum redundancy. In: AAAI, pp. 673–678 (2010)

    Google Scholar 

  29. Zhu, P., Hu, Q., Zhang, C., Zuo, W.: Coupled dictionary learning for unsupervised feature selection. In: AAAI, pp. 2422–2428 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Sijia Niu, Pengfei Zhu, Qinghua Hu & Hong Shi

Authors
  1. Sijia Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pengfei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qinghua Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pengfei Zhu .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Niu, S., Zhu, P., Hu, Q., Shi, H. (2019). Adaptive Graph Fusion for Unsupervised Feature Selection. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30484-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30483-6

  • Online ISBN: 978-3-030-30484-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation