Skip to main content
SpringerLink
Log in

Regularized Multiset Neighborhood Correlation Analysis for Semi-paired Multiview Learning

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12533))

Included in the following conference series:

Abstract

Canonical correlation analysis (CCA) is a popular and powerful technique for two-view dimension reduction and feature extraction. But, CCA is not able to directly handle more than two view data and has a rigorous assumption that all the samples from two different views are paired. However, practical multiple view data are often semi-paired. To address this problem, we in this paper propose a novel semi-paired multiview dimension reduction approach, which takes cross-view neighborhood relationship among semi-paired data and within-view global structure information into consideration. The proposed approach can not only deal with multiview (more than two) data, but also take sufficient advantage of unpaired multiview data and then mitigate overfitting effectively caused by the limited paired data. Experimental results on two benchmark data sets demonstrate the effectiveness of our proposed method.

Supported by the National Natural Science Foundation of China under Grant Nos. 61402203, 61703362 and 61906060, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20170513, the China Postdoctoral Science Foundation under Grant No. 2020M670995, and Yangzhou Science Project Foundation under Grant No. YZ2020173. It is also sponsored by Excellent Young Backbone Teacher (Qing Lan) Project and Scientific Innovation Project Fund of YZU under Grant No. 2017CXJ033.

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.

    https://www.nist.gov/itl/products-and-services/color-feret-database.

  2. 2.

    http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html.

References

  1. Li, Y., Yang, M., Zhang, Z.: A survey of multi-view representation learning. IEEE Trans. Knowl. Data Eng. 31(10), 1863–1883 (2019)

    Article  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. Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)

    Article  Google Scholar 

  4. He, X., Niyogi, P.: Locality preserving projections. In: NIPS, pp. 153–160 (2003)

    Google Scholar 

  5. Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)

    Article  Google Scholar 

  6. Blaschko, M.B., Lampert, C.H., Gretton, A.: Semi-supervised laplacian regularization of kernel canonical correlation analysis. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5211, pp. 133–145. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87479-9_27

    Chapter  Google Scholar 

  7. Kimura, A., et al.: SemiCCA: efficient semi-supervised learning of canonical correlations. In: ICPR, pp. 2933–2936. IEEE, Turkey (2010)

    Google Scholar 

  8. Chen, X., Chen, S., Xue, H., Zhou, X.: A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data. Pattern Recognit. 45(5), 2005–2018 (2012)

    Article  Google Scholar 

  9. Zhou, X., Chen, X., Chen, S.: Neighborhood correlation analysis for semi-paired two-view data. Neural Process. Lett. 37(3), 335–354 (2013)

    Article  Google Scholar 

  10. Yuan, Y.-H., Sun, Q.-S.: Multiset canonical correlations using globality preserving projections with applications to feature extraction and recognition. IEEE Trans. Neural Networks Learn. Syst. 25(6), 1131–1146 (2014)

    Article  Google Scholar 

  11. de Sa, V.R., Gallagher, P.W., Lewis, J.M., Malave, V.L.: Multi-view kernel construction. Mach. Learn. 79(1–2), 47–71 (2010)

    MathSciNet  Google Scholar 

  12. Yuan, Y.-H., Li, Y., Shen, X.-B., Sun, Q.-S., Yang, J.-L.: Laplacian multiset canonical correlations for multiview feature extraction and image recognition. Multimed. Tools Appl. 76(1), 731–755 (2017). https://doi.org/10.1007/s11042-015-3070-y

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Engineering, Yangzhou University, Yangzhou, China

    Yun-Hao Yuan, Zhaoqi Wu, Yun Li, Jipeng Qiang & Yi Zhu

  2. School of Computer Science and Technology, Fudan University, Shanghai, China

    Yun-Hao Yuan

  3. School of Computer Science, Jiangsu University, Zhenjiang, China

    Jianping Gou

Authors
  1. Yun-Hao Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaoqi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jipeng Qiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianping Gou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Li .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, China

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yuan, YH., Wu, Z., Li, Y., Qiang, J., Gou, J., Zhu, Y. (2020). Regularized Multiset Neighborhood Correlation Analysis for Semi-paired Multiview Learning. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63833-7_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63832-0

  • Online ISBN: 978-3-030-63833-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation