Advertisement

A BPSO-Based Tensor Feature Selection and Parameter Optimization Algorithm for Linear Support Higher-Order Tensor Machine

  • Qi YueEmail author
  • Jian-dong Shen
  • Ji Yao
  • Weixiao Zhan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

Feature selection is one of the key problems in the field of pattern recognition, computer vision, and image processing. With the continuous development of machine learning, the feature dimension of the object is becoming higher and higher, which leads to the problem of dimension disaster and over fitting. Tensor as a powerful expression of high dimensional data, can be a good solution to the above problems. Considering the much redundancy information in the tensor data and the model parameter largely affects the performance of linear support higher-order tensor machine (SHTM), a BPSO-based tensor feature selection and parameter optimization algorithm for SHTM is proposed. The algorithm can obtain better generalized accuracy by searching for the optimal model parameter and feature subset simultaneously. Experiments on USF gait recognition tensor set show that compared with the ordinary tensor classification algorithm and GA-TFS algorithm, this algorithm can shorten the time of large-scale data classification, reduce about 22.06% time-consuming, and improve the classification accuracy in a certain extent.

Keywords

Tensor feature selection Parameter optimization Support higher-order tensor machine Tensor rank-one decomposition 

References

  1. 1.
    Negi, P.S., Labate, D.: 3-D discrete shearlet transform and video processing. IEEE Trans. Image Process. 21(6), 2944–2954 (2012). A Publication of the IEEE Signal Processing SocietyMathSciNetCrossRefGoogle Scholar
  2. 2.
    Yan, T., Jones, B.E.: Color image processing and applications. Meas. Sci. Technol. 2, 222 (2001)Google Scholar
  3. 3.
    Gavrila, D.M.: The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999)CrossRefGoogle Scholar
  4. 4.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: MPCA: multilinear principal component analysis of tensor objects. IEEE Trans. Neural Netw. 19(1), 18–39 (2008)CrossRefGoogle Scholar
  5. 5.
    Yan, S., Xu, D., Yang, Q., et al.: Multilinear discriminant analysis for face recognition. IEEE Trans. Image Process. 16(1), 212–220 (2007). A Publication of the IEEE Signal Processing SocietyMathSciNetCrossRefGoogle Scholar
  6. 6.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition. IEEE Trans. Neural Netw. 20(1), 103–123 (2009)CrossRefGoogle Scholar
  7. 7.
    Wang, H., Ahuja, N.: Compact representation of multidimensional data using tensor rank-one decomposition. In: International Conference on Pattern Recognition, vol. 1, pp. 44–47. IEEE (2004)Google Scholar
  8. 8.
    Geng, X., Smith-Miles, K., Zhou, Z.H., et al.: Face image modeling by multilinear subspace analysis with missing values. IEEE Trans. Syst. Man Cybern. Part B Cybern. 41(3), 881–892 (2011). A Publication of the IEEE Systems Man & Cybernetics SocietyCrossRefGoogle Scholar
  9. 9.
    Tao, D., Li, X., Hu, W., et al.: Supervised tensor learning. In: Proceedings of the IEEE International Conference on Data Mining, pp. 450–457 (2005)Google Scholar
  10. 10.
    Fei, W., Yanan, L., Yueting, Z.: Tensor-based transductive learning for multimodality video semantic concept detection. IEEE Trans. Multimed. 11, 868–878 (2009)CrossRefGoogle Scholar
  11. 11.
    Liu, Y., Liu, Y., Chan, K.C.C.: Tensor-based locally maximum margin classifier for image and video classification. Comput. Vis. Image Underst. 115(115), 300–309 (2011)CrossRefGoogle Scholar
  12. 12.
    Signoretto, M., Lathauwer, L.D., Suykens, J.A.K.: A kernel-based framework to tensorial data analysis. Neural Netw. Off. J. Int. Neural Netw. Soc. 24(8), 861–874 (2011)CrossRefGoogle Scholar
  13. 13.
    Signoretto, M., Olivetti, E., De Lathauwer, L., et al.: Classification of multichannel signals with cumulant-based kernels. IEEE Trans. Signal Process. 60(5), 2304–2314 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hao, Z., He, L., Chen, B., et al.: A linear support higher-order tensor machine for classification. IEEE Trans. Image Process. 22(7), 2911–2920 (2013). A Publication of the IEEE Signal Processing SocietyCrossRefGoogle Scholar
  15. 15.
    Savicky, P., Vomlel, J.: Exploiting tensor rank-one decomposition in probabilistic inference. Kybernetika 43(5), 747–764 (2006)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Vinh, L.T., Lee, S., Park, Y.T., et al.: A novel feature selection method based on normalized mutual information. Appl. Intell. 37(1), 100–120 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.China Academy of Information and Communications TechnologyBeijingChina

Personalised recommendations