Skip to main content
SpringerLink
Log in

Multiview Dimension Reduction Based on Sparsity Preserving Projections

  • Conference paper
  • First Online:
Image and Video Technology (PSIVT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11854))

Included in the following conference series:

  • 1264 Accesses

Abstract

In this paper, we focus on boosting the subspace learning by exploring the complimentary and compatible information from multi-view features. A novel multi-view dimension reduction method is proposed named Multiview Sparsity Preserving Projection (MSPP) for this task. MSPP aims to seek a set of linear transforms to project multi-view features into subspace where the sparse reconstructive weights of multi-view features are preserved as much as possible. And the Hilbert Schmidt Independence Criterion (HSIC) is utilized as a dependence term to explore the compatible and complementary information from multi-view features. An efficient alternative iterating optimization is presented to obtain the optimal solution of MSPP. Experiments on image datasets and multi-view textual datasets well demonstrate the excellent performance of MSPP.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis-a brief tutorial. Inst. Signal Inf. Process. 18, 1–8 (1998)

    Google Scholar 

  2. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)

    Article  Google Scholar 

  3. Cai, D., He, X., Han, J.: Spectral regression: a unified approach for sparse subspace learning. In: Seventh IEEE International Conference on Data Mining (ICDM 2007), pp. 73–82. IEEE (2007)

    Google Scholar 

  4. Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality sensitive discriminant analysis. In: IJCAI, vol. 2007, pp. 1713–1726 (2007)

    Google Scholar 

  5. Cao, X., Zhang, C., Fu, H., Liu, S., Zhang, H.: Diversity-induced multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–594 (2015)

    Google Scholar 

  6. Chapelle, O., Haffner, P., Vapnik, V.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Networks 10(5), 1055–1064 (1999)

    Article  Google Scholar 

  7. 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 

  8. Gao, X., Xiao, B., Tao, D., Li, X.: Image categorization: graph edit direction histogram. Pattern Recogn. 41(10), 3179–3191 (2008)

    Article  Google Scholar 

  9. Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.: Measuring statistical dependence with Hilbert-Schmidt norms. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 63–77. Springer, Heidelberg (2005). https://doi.org/10.1007/11564089_7

    Chapter  Google Scholar 

  10. He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005) Volume 1, vol. 2, pp. 1208–1213. IEEE (2005)

    Google Scholar 

  11. He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, pp. 153–160 (2004)

    Google Scholar 

  12. Hu, H., Lin, Z., Feng, J., Zhou, J.: Smooth representation clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3834–3841 (2014)

    Google Scholar 

  13. Joachims, T.: Transductive inference for text classification using support vector machines (1999)

    Google Scholar 

  14. Kan, M., Shan, S., Zhang, H., Lao, S., Chen, X.: Multi-view discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 188–194 (2015)

    Article  Google Scholar 

  15. 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 

  16. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  17. Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sensing 28(5), 823–870 (2007)

    Article  Google Scholar 

  18. Nie, F., Cai, G., Li, J., Li, X.: Auto-weighted multi-view learning for image clustering and semi-supervised classification. IEEE Trans. Image Process. 27(3), 1501–1511 (2017)

    Article  MathSciNet  Google Scholar 

  19. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  20. Qiao, L., Chen, S., Tan, X.: Sparsity preserving projections with applications to face recognition. Pattern Recogn. 43(1), 331–341 (2010)

    Article  Google Scholar 

  21. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  22. Tao, D., Jin, L.: Discriminative information preservation for face recognition. Neurocomputing 91(2), 11–20 (2012)

    Article  Google Scholar 

  23. Wang, H., Feng, L., Zhang, J., Liu, Y.: Semantic discriminative metric learning for image similarity measurement. IEEE Trans. Multimedia 18(8), 1579–1589 (2016). https://doi.org/10.1109/TMM.2016.2569412

    Article  Google Scholar 

  24. Wang, H., Feng, L., Yu, L., Zhang, J.: Multi-view sparsity preserving projection for dimension reduction. Neurocomputing 216, 286–295 (2016)

    Article  Google Scholar 

  25. Wang, H., Li, H., Fu, X.: Auto-weighted mutli-view sparse reconstructive embedding. Multimedia Tools Appl. 1–15 (2019)

    Google Scholar 

  26. Wang, H., Li, H., Peng, J., Fu, X.: Multi-feature distance metric learning for non-rigid 3D shape retrieval. Multimedia Tools Appl. 1–16 (2019)

    Google Scholar 

  27. Wang, H., Lin, F., Meng, X., Chen, Z., Yu, L., Zhang, H.: Multi-view metric learning based on KL-divergence for similarity measurement. Neurocomputing 238(C), 269–276 (2017)

    Article  Google Scholar 

  28. Wang, H., Peng, J., Fu, X.: Co-regularized multi-view sparse reconstruction embedding for dimension reduction. Neurocomputing 347, 191–199 (2019)

    Article  Google Scholar 

  29. Wold, S.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1), 37–52 (1987)

    Article  Google Scholar 

  30. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2008)

    Article  Google Scholar 

  31. Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 40(6), 1438–1446 (2010)

    Article  Google Scholar 

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

  33. Xu, D., Yan, S., Tao, D., Lin, S., Zhang, H.J.: Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval. IEEE Trans. Image Process. 16(11), 2811–2821 (2007)

    Article  MathSciNet  Google Scholar 

  34. Yang, J., Kai, Y., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp. 1794–1801 (2009)

    Google Scholar 

  35. Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Sci. Comput. 26(1), 313–338 (2004)

    Article  MathSciNet  Google Scholar 

  36. 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 

Download references

Acknowledgment

This work is supported by the Natural Science Foundation of China [No. 61572099]; Major National Science and Technology of China 2018ZX04011001-007, 2018ZX04016001-011.

Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian District of Ganjingzi City Road 2, Dalian, 116024, China

    Haohao Li, Yu Cai, Guohui Zhao, Zhixun Su & Ximin Liu

  2. Dalian Shipbuilding Industry Co., Dalian, China

    Hu Lin

Authors
  1. Haohao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guohui Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhixun Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ximin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhixun Su .

Editor information

Editors and Affiliations

  1. Chonnam National University, Gwangju, Korea (Republic of)

    Chilwoo Lee

  2. Dalian University of Technology, Dalian, China

    Zhixun Su

  3. National Institute of Informatics, Tokyo, Japan

    Akihiro Sugimoto

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

Li, H., Cai, Y., Zhao, G., Lin, H., Su, Z., Liu, X. (2019). Multiview Dimension Reduction Based on Sparsity Preserving Projections. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34879-3_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34878-6

  • Online ISBN: 978-3-030-34879-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation