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An inexact splitting method for the subspace segmentation from incomplete and noisy observations

Author

Listed:
  • Renli Liang

    (Shanghai University)

  • Yanqin Bai

    (Shanghai University)

  • Hai Xiang Lin

    (Delft University of Technology)

Abstract

Subspace segmentation is a fundamental issue in computer vision and machine learning, which segments a collection of high-dimensional data points into their respective low-dimensional subspaces. In this paper, we first propose a model for segmenting the data points from incomplete and noisy observations. Then, we develop an inexact splitting method for solving the resulted model. Moreover, we prove the global convergence of the proposed method. Finally, the inexact splitting method is implemented on the clustering problems in synthetic and benchmark data, respectively. Numerical results demonstrate that the proposed method is computationally efficient, robust as well as more accurate compared with the state-of-the-art algorithms.

Suggested Citation

  • Renli Liang & Yanqin Bai & Hai Xiang Lin, 2019. "An inexact splitting method for the subspace segmentation from incomplete and noisy observations," Journal of Global Optimization, Springer, vol. 73(2), pages 411-429, February.
    • Handle: RePEc:spr:jglopt:v:73:y:2019:i:2:d:10.1007_s10898-018-0684-4
    DOI: 10.1007/s10898-018-0684-4
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    References listed on IDEAS

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    1. Heinz H. Bauschke & Patrick L. Combettes, 2001. "A Weak-to-Strong Convergence Principle for Fejér-Monotone Methods in Hilbert Spaces," Mathematics of Operations Research, INFORMS, vol. 26(2), pages 248-264, May.
    2. P. Tseng, 2000. "Nearest q-Flat to m Points," Journal of Optimization Theory and Applications, Springer, vol. 105(1), pages 249-252, April.
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