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An Improved Canonical Correlation Analysis Method with Adaptive Graph Learning

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 89))

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Abstract

Graph learning describes the local structure information hidden in samples by adjacent matrices and the key step is determining the nearest samples where the local and sparse preserving methods are widely adopted. Inspired by the adaptive neighbor searching strategy in clustering tasks, an improved canonical correlation analysis method is proposed to extract more discriminative features from multiple datasets through faithful neighbor sample selection and reliable pseudo label indicator prediction. Experimental results on two handwritten numeral datasets show that our method is better than traditional approaches.

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References

  1. Sun, Q., Zeng, S., Liu, Y., Heng, P., Xia, D.: A new method of feature fusion and its application in image recognition. Pattern Recogn. 38(12), 2437–2448 (2005)

    Article  Google Scholar 

  2. Uurtio, V., Monteiro, J.M., Kandola, J., et al: A tutorial on canonical correlation methods. ACM Comput. Surv. 50(6), Article 95 (2017)

    Google Scholar 

  3. Lee, G., Singanamalli, A., Wang, H., et al.: Supervised multi-view canonical correlation analysis (sMVCCA): integrating histologic and proteomic features for predicting. IEEE Trans. Med. Imaging 34(1), 284–297 (2015)

    Article  Google Scholar 

  4. Shen, X., Sun, Q.: A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction. J. Vis. Commun. Image Representation 83(3), 1894–1904 (2014)

    Article  Google Scholar 

  5. Xing, X., Wang, K., Yan, T., Lv, Z.: Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recogn. 50, 107–117 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Han, L., Jing, X., Wu, F.: Multi-view local discrimination and canonical correlation analysis for image classification. Neurocomputing 275, 1087–1098 (2018)

    Article  Google Scholar 

  8. Gao, X., Sun, Q., Xu, H.: Multiple-rank supervised canonical correlation analysis for extraction, fusion and recognition. Expert Syst. Appl. 84, 171–185 (2017)

    Article  Google Scholar 

  9. Desai, N., Seghouane, A., Palaniswami, M.: Algorithms for two dimensional multi set canonical correaltion analysis. Pattern Recogn. Lett. 111, 101–108 (2018)

    Article  Google Scholar 

  10. Andrew, G., Arora, R., Bilmes, J., et al.: Deep canonical correlation analysis. In: International Conference on Machine Learning, Atlanta, Georgia, USA, pp. 1247–1255 (2013)

    Google Scholar 

  11. Liu, Y., Li, Y., Yuan, Y.H., et al.: Supervised deep canonical correlation analysis for multiview feature learning. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) Neural Information Processing, ICONIP 2017. LNCS, vol. 10639, pp. 575–582. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70136-3_61

  12. Ji, Z., Yu, X., Pang, Y.: Zero-shot learning with deep canonical correlation analysis. In: Yang, J., et al. (eds.) CCCV 2017. CCIS, vol. 773, pp. 209–219. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-7305-2_19

    Chapter  Google Scholar 

  13. Yu, Y., Tang, S., Aizawa, K., et al.: Category-based deep CCA for fine-grained venue discovery form multimodal data. IEEE Trans. Neural Netw. Learn. Syst. 30(4), 1250–1258 (2019)

    Article  MathSciNet  Google Scholar 

  14. Nie, Y., 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 (2018)

    Article  MathSciNet  Google Scholar 

  15. Wen, J., Xu, Y., Liu, H.: Incomplete multiview spectral clustering with adaptive graph learning. IEEE Trans. Cybern. 50(4), 1418–1429 (2020)

    Article  Google Scholar 

  16. Hou, S., Liu, H., Wang, X.: Pseudo label guided subspace learning for multi-view data. In: Lin, Z., et al. (eds.) PRCV 2019. LNCS, vol. 11859, pp. 75–83. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31726-3_7

    Chapter  Google Scholar 

  17. Wang, S., Lu, J., Gu, X., et al.: Unsupervised discriminant canonical correlation analysis based on spectral clustering. Neurocomputing 171, 425–433 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by Natural Science Foundation of Anhui Province under Grant 1808085QF210, the Key Project of Natural Science of Anhui Provincial Department of Education under Grant KJ2018A0043, and in part by Educational Commission of Anhui Province of China under Grant 2020sxzx54, Natural Science Projects of Maanshan Technical College under Grant MKJ2021007.

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Authors and Affiliations

  1. Maanshan Technical College, Maanshan, 243031, China

    Chuanxin Yuan

  2. Anhui University of Technology, Maanshan, 243031, China

    Shudong Hou

Authors
  1. Chuanxin Yuan
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  2. Shudong Hou
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Corresponding author

Correspondence to Chuanxin Yuan .

Editor information

Editors and Affiliations

  1. Guizhou University, Guiyang, China

    Quan Xie

  2. Computer Science and Mathematics, University of São Paulo (USP), Ribeirão Preto, São Paulo, Brazil

    Liang Zhao

  3. College of Electrical and Information, Hunan University, Changsha, China

    Kenli Li

  4. Department of Mathematics, NIT Jalandhar, Jalandhar, India

    Anupam Yadav

  5. Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Yuan, C., Hou, S. (2022). An Improved Canonical Correlation Analysis Method with Adaptive Graph Learning. In: Xie, Q., Zhao, L., Li, K., Yadav, A., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-89698-0_45

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