• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Robust tensor factorization using maximum correntropy criterion

    Author(s)
    Zhang, M
    Gao, Y
    Sun, C
    La Salle, J
    Liang, J
    Griffith University Author(s)
    Gao, Yongsheng
    Zhang, Lena
    Year published
    2016
    Metadata
    Show full item record
    Abstract
    Traditional tensor decomposition methods, e.g., two dimensional principle component analysis (2DPCA) and two dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE) and are sensitive to outliers. In this paper, we propose a new robust tensor factorization method using maximum correntropy criterion (MCC) to improve the robustness of traditional tensor decomposition methods. A half-quadratic optimization algorithm is adopted to effectively optimize the correntropy objective function in an iterative manner. It can effectively improve the robustness of a tensor decomposition method to outliers without ...
    View more >
    Traditional tensor decomposition methods, e.g., two dimensional principle component analysis (2DPCA) and two dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE) and are sensitive to outliers. In this paper, we propose a new robust tensor factorization method using maximum correntropy criterion (MCC) to improve the robustness of traditional tensor decomposition methods. A half-quadratic optimization algorithm is adopted to effectively optimize the correntropy objective function in an iterative manner. It can effectively improve the robustness of a tensor decomposition method to outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracy rate on handwritten digit recognition.
    View less >
    Conference Title
    Proceedings - International Conference on Pattern Recognition
    Volume
    0
    DOI
    https://doi.org/10.1109/ICPR.2016.7900290
    Subject
    Pattern recognition
    Numerical computation and mathematical software
    Publication URI
    http://hdl.handle.net/10072/338808
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander