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Multi-kernel and Multi-task Learning for Radar Target Recognition

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IoT as a Service (IoTaaS 2020)

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Abstract

In this paper, a multiple kernel and multiple task learning framework (MKMTL) is proposed. To improve the interpretability of input data and adapt to different data sets, a weighted data-dependent kernel function is proposed and extended to multiple kernel functions. To fully reveal and utilize the shared information among different radar targets, multi-task learning framework is proposed. In this paper, a larger class of mixed norm penalty is adopted. It can increase the flexibility of MKMTL model. To verify the performance of the proposed model, measured MSTAR SAR public database is conducted. Experimental results demonstrate that the proposed method can effectively utilize the shared or potential information among different tasks and exhibits a better recognition performance compared with several popular existing recognition methods.

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References

  1. Liu, A.N., et al.: HEp-2 cells classification via clustered multi-task learning. Neurocomputing 195(C), 195–201 (2016)

    Article  Google Scholar 

  2. Kang, M., et al.: Synthetic aperture radar target recognition with feature fusion based on a stacked autoencoder. Sensors 17(1), 192 (2017)

    Article  Google Scholar 

  3. Wang, S.N., et al.: SAR image target recognition via complementary spatial pyramid coding. Neurocomputing 196(C), 125–132 (2016)

    Google Scholar 

  4. Odysseas, K.S., Nabil, A.: Fusing deep learning and sparse coding for SAR ATR. IEEE Trans. Aerosp. Electron. Syst. 55(2), 785–797 (2019)

    Article  Google Scholar 

  5. Tian, Z., et al.: Classification via weighted kernel CNN: application to SAR target recognition. Int. J. Remote Sens. 39(3), 1–20 (2018)

    Google Scholar 

  6. Su, H.J., et al.: Kernel collaborative representation with local correlation features for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(2), 1230–1241 (2019)

    Article  Google Scholar 

  7. Wei, C.C.: Wavelet kernel support vector machines forecasting techniques: case study on water-level predictions during typhoons. Expert Syst. Appl. 39(5), 5189–5199 (2012)

    Article  Google Scholar 

  8. Rakotomamonjy, A., et al.: SimpleMKL. J. Mach. Learn. Res. 9(3), 2491–2521 (2008)

    MathSciNet  MATH  Google Scholar 

  9. Micchelli, C.A., Pontil, M.: Learning the kernel function via regularization. J. Mach. Learn. Res. 6(6), 1099–1125 (2005)

    MathSciNet  MATH  Google Scholar 

  10. Xu, Y.Y., et al.: An efficient renovation on kernel fisher discriminant analysis and face recognition experiments. Pattern Recogn. 37(10), 2091–2094 (2004)

    Article  Google Scholar 

  11. Wang, Q.W., Gu, Y.F., Tuia, D.: Discriminative multiple kernel learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 54(7), 3912–3927 (2016)

    Article  Google Scholar 

  12. Gu, Y.F., et al.: Multiple kernel learning via low-rank nonnegative matrix factorization for classification of hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2739–2751 (2015)

    Article  Google Scholar 

  13. Gu, Y.F., Wang, Q.W., Xie, B.Q.: Multiple kernel sparse representation for airborne LiDAR data classification. IEEE Trans. Geosci. Remote Sens. PP(99), 1–21 (2016)

    Google Scholar 

  14. Karine, A., et al.: Radar target recognition using salient keypoint descriptors and multitask sparse representation. Remote Sens. 10(6), 843 (2018)

    Article  Google Scholar 

  15. Zhang, X.Z., et al.: Two-stage multi-task representation learning for synthetic aperture radar (SAR) target Images classification. Sensors. 17(11), 2506 (2017)

    Article  Google Scholar 

  16. Ozawa, S., Roy, A., Roussinov, D.: A multitask learning model for online pattern recognition. IEEE Trans. Neural Networks 20(3), 430–445 (2009)

    Article  Google Scholar 

  17. Du, L., et al.: Bayesian spatiotemporal multitask learning for radar HRRP target recognition. IEEE Trans. Signal Process. 59(7), 3182–3196 (2011)

    Article  MathSciNet  Google Scholar 

  18. Zheng, H., et al.: Multi-task model for simultaneous face identification and facial expression recognition. Neurocomputing. 171(C), 515–523 (2016)

    Article  Google Scholar 

  19. Dong, G.G., et al.: SAR target recognition via joint sparse representation of monogenic signal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(7), 3316–3328 (2015)

    Article  Google Scholar 

  20. Rakotomamonjy, A., et al.: Penalty for sparse linear and sparse multiple kernel multitask learning. IEEE Trans. Neural Networks 22(8), 1307–1320 (2011)

    Article  Google Scholar 

  21. Lounici, K., et al.: Taking advantage of sparsity in multi-task Learning. In: COLT 2009 (2009)

    Google Scholar 

  22. Xiong, H., Swamy, M.N., Ahmad, M.O.: Optimizing the kernel in the empirical feature space. IEEE Trans. Neural Networks 16(2), 460–474 (2005)

    Article  Google Scholar 

  23. Cristianini, N., et al.: On kernel-target alignment. Adv. Neural. Inf. Process. Syst. 179(5), 367–373 (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Academy of Space Electronic Information Technology, Xi’an, 710100, China

    Cong Li, Xianyu Wang & Xu Yang

Authors
  1. Cong Li
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  2. Xianyu Wang
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  3. Xu Yang
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Editor information

Editors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Bo Li

  2. State Key Laboratory of ISN, Xidian Univ​ersity, Xi'an, China

    Changle Li

  3. Northwestern Polytechnical University, Xi'an, China

    Mao Yang

  4. School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China

    Zhongjiang Yan

  5. Northwest University, Xi'an, China

    Jie Zheng

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, C., Wang, X., Yang, X. (2021). Multi-kernel and Multi-task Learning for Radar Target Recognition. In: Li, B., Li, C., Yang, M., Yan, Z., Zheng, J. (eds) IoT as a Service. IoTaaS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-67514-1_31

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  • DOI: https://doi.org/10.1007/978-3-030-67514-1_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67513-4

  • Online ISBN: 978-3-030-67514-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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