Advertisement

Supervised Two-Dimensional CCA for Multiview Data Representation

  • Yun-Hao Yuan
  • Hui Zhang
  • Yun Li
  • Jipeng Qiang
  • Wenyan Bao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)

Abstract

Since standard canonical correlation analysis (CCA) works with vectorized representations of data, an limitation is that it may suffer small sample size problems. Moreover, two-dimensional CCA (2D-CCA) extracts unsupervised features and thus ignores the useful prior class information. This makes the extracted features by 2D-CCA hard to discriminate the data from different classes. To solve this issue, we simultaneously take the prior class information of intra-view and inter-view samples into account and propose a new 2D-CCA method referred to as supervised two-dimensional CCA (S2CCA), which can be used for multi-view feature extraction and classification. The method we propose is available to face recognition. To verify the effectiveness of the proposed method, we perform a number of experiments on the AR, AT&T, and CMU PIE face databases. The results show that the proposed method has better recognition accuracy than other existing multi-view feature extraction methods.

Keywords

Multi-view learning Canonical correlation analysis Two-dimensional analysis Dimensionality reduction 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61402203, 61472344, 61611540347, 61703362, Natural Science Fund of Jiangsu under Grant Nos. BK20161338, BK20170513, and Yangzhou Science Fund under Grant Nos. YZ2017292, YZ2016238. Moreover, it is also sponsored by the Excellent Young Backbone Teacher (Qing Lan) Fund and Scientific Innovation Research Fund of Yangzhou University under Grant No. 2017CXJ033.

References

  1. 1.
    An, L., Bhanu, B.: Face image super-resolution using 2D CCA. Sig. Process. 103, 184–194 (2014)CrossRefGoogle Scholar
  2. 2.
    Andrew, G., Arora, R., Bilmes, J.A., Livescu, K.: Deep canonical correlation analysis. In: ICML, pp. 1247–1255 (2013)Google Scholar
  3. 3.
    Gao, X., Sun, Q., Xu, H., Li, Y.: 2D-LPCCA and 2D-SPCCA: two new canonical correlation methods for feature extraction, fusion and recognition. Neurocomputing 284, 148–159 (2018)CrossRefGoogle Scholar
  4. 4.
    Hardoon, D.R., Szedmak, S.R., Shawe-Taylor, J.R.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004)CrossRefGoogle Scholar
  5. 5.
    Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)CrossRefGoogle Scholar
  6. 6.
    Lee, S.H., Choi, S.: Two-dimensional canonical correlation analysis. IEEE Sig. Process. Lett. 14(10), 735–738 (2007)CrossRefGoogle Scholar
  7. 7.
    Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE T-PAMI 13(3), 252–264 (1991)CrossRefGoogle Scholar
  8. 8.
    Sharma, A., Kumar, A., Daume, H., Jacobs, D.W.: Generalized multiview analysis: a discriminative latent space. In: CVPR, pp. 2160–2167 (2012)Google Scholar
  9. 9.
    Singh, A., Dutta, M.K., ParthaSarathi, M., Uher, V., Burget, R.: Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput. Methods Programs Biomed. 124, 108–120 (2016)CrossRefGoogle Scholar
  10. 10.
    Sun, Q.S., Liu, Z.D., Heng, P.A., Xia, D.S.: A theorem on the generalized canonical projective vectors. Pattern Recognit. 38(3), 449–452 (2005)CrossRefGoogle Scholar
  11. 11.
    Sun, T., Chen, S., Yang, J., Shi, P.: A novel method of combined feature extraction for recognition. In: ICDM, pp. 1043–1048 (2008)Google Scholar
  12. 12.
    Yuan, Y., Lu, P., Xiao, Z., Liu, J., Wu, X.: A novel supervised CCA algorithm for multiview data representation and recognition. In: CCBR, pp. 702–709 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yun-Hao Yuan
    • 1
  • Hui Zhang
    • 1
  • Yun Li
    • 1
  • Jipeng Qiang
    • 1
  • Wenyan Bao
    • 1
  1. 1.School of Information EngineeringYangzhou UniversityYangzhouChina

Personalised recommendations