Skip to main content
SpringerLink
Log in

A label embedding kernel method for multi-view canonical correlation analysis

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a novel label embedding kernel method (LEKM), which is capable of well capturing intrinsic discriminating structure of samples with the help of class label information. LEKM can efficiently project training samples into a label kernel space according to a label-based unit hypersphere model. However, it is difficult for LEKM to map out-of-sample data into the label kernel space due to the lack of out-of-sample class label information. To solve the problem, we give a simple but effective fuzzy projection strategy (FPS) that can approximately project out-of-sample data into the label kernel space according to similarity principle of sample distribution. With LEKM and FPS, we present a label embedding kernel multi-view canonical correlation analysis (LEKMCCA) algorithm, which can extract nonlinear canonical features with well discriminating power. The algorithm is applied to object, face and handwritten image recognition. Extensive experiments on several real-world image datasets have demonstrated the superior performance of the algorithm.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Dataset available at: http://www.eecs.qmul.ac.uk/~sgg/QMUL_FaceDataset/

  2. Dataset available at: http://archive.ics.uci.edu/ml/datasets/Semeion+Handwritten+Digit

  3. The MvDA code comes from the website: http://vipl.ict.ac.cn/resources/codes

  4. The GMMFA code comes from the website: http://www.cs.umd.edu/~bhokaal/Research.htm

References

  1. Golub GH, Van Loan CF (2012) Matrix computations. JHU Press

  2. Hotelling H (1936) Relations between two sets of variates. Biometrika 28(34):321–377

    Article  MATH  Google Scholar 

  3. Hu E, Chen S, Yu J, Qiao L (2011) Two-stage nonparametric kernel leaning: from label propagation to kernel propagation. Neurocomputing 74(17):2725–2733

    Article  Google Scholar 

  4. Kan M, Shan S, Zhang H, Lao S, Chen X (2016) Multi-view discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194

    Article  Google Scholar 

  5. Kettenring JR (1971) Canonical analysis of several sets of variables. Biometrika 58(3):433–451

    Article  MathSciNet  MATH  Google Scholar 

  6. Larson NB, Jenkins GD, Larson MC, Vierkant RA, Sellers TA, Phelan CM, Wentzensen N (2014) Kernel canonical correlation analysis for assessing gene–gene interactions and application to ovarian cancer. Eur J Hum Genet 22(1):126–131

    Article  Google Scholar 

  7. Lee KJ, Kwon MH, Jung MI (2014) Seasonal forecast skill of East Asia summer monsoon using CCA associated with ENSO. In: General Assembly Conference Abstracts. Vienna, Austria, May, pp 2184

  8. Liu HD, Yang M, Gao Y, Yin Y, Chen L (2014) Bilinear discriminative dictionary learning for face recognition. Pattern Recogn 47(5):1835–1845

    Article  Google Scholar 

  9. Melzer T, Reiter M, Bischof H (2003) Appearance models based on kernel canonical correlation analysis. Pattern Recogn 36(9):1961–1971

    Article  MATH  Google Scholar 

  10. Nicolaou MA, Pavlovic V, Pantic M (2014) Dynamic probabilistic CCA for analysis of affective behavior and fusion of continuous annotations. IEEE Trans Pattern Anal Mach Intell 36(7):1299–1311

    Article  Google Scholar 

  11. Nielsen AA (2002) Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data. IEEE Trans Image Process 11(3):293–305

    Article  Google Scholar 

  12. Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  13. Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, pp 2160–2167

  14. Shen XB, Sun QS, Yuan YH (2015) A unified multiset canonical correlation analysis framework based on graph embedding for multiple feature extraction. Neurocomputing 148:397–408

    Article  Google Scholar 

  15. Shi Y, Ji H (2014) Kernel canonical correlation analysis for specific radar emitter identification. Electron Lett 50(18):1318–1320

    Article  Google Scholar 

  16. Sun T, Chen S (2007) Locality preserving CCA with applications to data visualization and pose estimation. Image Vis Comput 25(5):531–543

    Article  Google Scholar 

  17. Sun QS, Zeng SG, Liu Y, Heng PA, Xia DS (2005a) A new method of feature fusion and its application in image recognition. Pattern Recogn 38(12):2437–2448

    Article  Google Scholar 

  18. Sun QS, Liu Z, Heng PA, Xia DS (2005b) A theorem on the generalized canonical projective vectors. Pattern Recogn 38(3):449–452

    Article  MATH  Google Scholar 

  19. Sun TK, Chen SC, Jin Z, Yang JY (2007) Kernelized discriminative canonical correlation analysis. In: IEEE International Conference on Wavelet Analysis and Pattern Recognition, pp 1283-1287

  20. Sun T, Chen S, Yang J, Shi P (2008) A supervised combined feature extraction method for recognition. In: Procedings of the IEEE International Conference on Data Mining. Pisa, pp 1043-1048

  21. Vaerenbergh VS, Via J, Santamaria I (2013) Blind identification of SIMO Wiener systems based on kernel canonical correlation analysis. Signal Process, IEEE Transactions on 61(9):2219-2230

  22. Wilks DS (2014) Probabilistic canonical correlation analysis forecasts, with application to tropical Pacific sea-surface temperatures. Int J Climatol 34(5):1405–1413

    Article  Google Scholar 

  23. Yan C, Zhang Y, Dai F, Li L (2013a) Highly parallel framework for HEVC motion estimation on many-core platform. In: Proceedings of Data Compression Conference (DCC), pp 63-72

  24. Yan C, Zhang Y, Dai F, Li L (2013b) Efficient parallel framework for HEVC deblocking filter on many-core platform. In: Proceedings of Data Compression Conference (DCC), pp 530-530

  25. Yan Y, Ricci E, Subramanian R, Liu G, Sebe N (2014a) Multitask linear discriminant analysis for view invariant action recognition. IEEE Trans Image Process 23(12):5599–5611

    Article  MathSciNet  Google Scholar 

  26. Yan Y, Ricci E, Liu G, Subramanian R, Sebe N (2014b) Clustered multi-task linear discriminant analysis for view invariant color-depth action recognition. In: Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), pp 3493-3498

  27. Yan Y, Subramanian R, Ricci E, Lanz O, Sebe N (2014c) Evaluating Multi-task Learning for Multi-view Head-Pose Classification in Interactive Environments. In: Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), pp 4182-4187

  28. Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014d) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  29. Yan C, Zhang Y, Xu J, Dai F, Li L, Dai Q, Wu F (2014e) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

    Article  Google Scholar 

  30. Yan C, Zhang Y, Dai F, Wang X, Li L, Dai Q (2014f) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50(5):367–368

    Article  Google Scholar 

  31. Yan C, Zhang Y, Dai F, Zhang J, Li L, Dai Q (2014g) Efficient parallel HEVC intra-prediction on many-core processor. Electron Lett 50(11):805–806

    Article  Google Scholar 

  32. Yan Y, Ricci E, Liu G, Sebe N (2015a) Egocentric daily activity recognition via multitask clustering. IEEE Trans Image Process 24(10):2984–2995

    Article  MathSciNet  Google Scholar 

  33. Yan Y, Yang Y, Meng D, Liu G, Tong W, Hauptmann AG, Sebe N (2015b) Event oriented dictionary learning for complex event detection. IEEE Trans Image Process 24(6):1867–1878

    Article  MathSciNet  Google Scholar 

  34. Yan Y, Ricci E, Subramanian R, Liu G, Lanz O, Sebe N (2016) A multi-task learning framework for head pose estimation under target motion. IEEE Trans Pattern Anal Mach Intell 38(6):1070–1083

    Article  Google Scholar 

  35. Yuan YH, Sun QS (2014) Graph regularized multiset canonical correlations with applications to joint feature extraction. Pattern Recogn 47(12):3907–3919

    Article  Google Scholar 

  36. Yuan YH, Sun QS, Zhou Q, Xia DS (2011) A novel multiset integrated canonical correlation analysis framework and its application in feature fusion. Pattern Recogn 44(5):1031–1040

    Article  MATH  Google Scholar 

  37. Yuan YH, Sun QS, Ge HW (2014) Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition. Pattern Recogn 47(3):1411–1424

    Article  MATH  Google Scholar 

  38. Zheng W, Zhou X, Zou C, Zhao L (2006) Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Trans Neural Netw 17(1):233–238

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the Graduate Innovation Project of Jiangsu Province under Grant No. KYLX15_1169, the 111 Project under Grant No. B12018, and PAPD of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    Shuzhi Su, Hongwei Ge & Yun-Hao Yuan

Authors
  1. Shuzhi Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongwei Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun-Hao Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Ge.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Su, S., Ge, H. & Yuan, YH. A label embedding kernel method for multi-view canonical correlation analysis. Multimed Tools Appl 76, 13785–13803 (2017). https://doi.org/10.1007/s11042-016-3786-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3786-3

Keywords

Search

Navigation