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Manifold Regularization Nonnegative Triple Decomposition of Tensor Sets for Image Compression and Representation

Author

Listed:
  • Fengsheng Wu

    (Yunnan University)

  • Chaoqian Li

    (Yunnan University)

  • Yaotang Li

    (Yunnan University)

Abstract

The image processing usually depends on exploring the structure and the geometric information of the tensor objects generated by image data. In the process, the decomposition of the tensor objects is very significant for the dimension reduction and the low-rank representation of image data. In this paper, based on the triple decomposition of third-order tensors and the correlation between different nonnegative tensor objects, a nonnegative triple decomposition model with manifold regularization terms is constructed. Then, an algorithm for the manifold regularization nonnegative triple decomposition is proposed, and the convergence of the algorithm is discussed. Furthermore, experiments on some real-world image data sets are given to illustrate the feasibility and effectiveness of the proposed algorithms.

Suggested Citation

  • Fengsheng Wu & Chaoqian Li & Yaotang Li, 2022. "Manifold Regularization Nonnegative Triple Decomposition of Tensor Sets for Image Compression and Representation," Journal of Optimization Theory and Applications, Springer, vol. 192(3), pages 979-1000, March.
    • Handle: RePEc:spr:joptap:v:192:y:2022:i:3:d:10.1007_s10957-022-02001-6
    DOI: 10.1007/s10957-022-02001-6
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