• 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
  • Kernel Mean P Power Error Loss for Robust Two-Dimensional Singular Value Decomposition

    Author(s)
    Zhang, M
    Gao, Y
    Sun, C
    Blumenstein, M
    Griffith University Author(s)
    Gao, Yongsheng
    Year published
    2019
    Metadata
    Show full item record
    Abstract
    Traditional matrix-based dimensional reduction methods, e.g., two-dimensional principal component analysis (2DPCA) and two-dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE), which is sensitive to outliers. To overcome this problem, in this paper we propose a new robust 2DSVD method based on the kernel mean p power error loss (KMPE-2DSVD). Different from the MSE and the correntropy based ones which are second order statistics based measurements, the KMPE-2DSVD is based on the non-second order statistics in the kernel space, and thus is more flexible in controlling the representation error. ...
    View more >
    Traditional matrix-based dimensional reduction methods, e.g., two-dimensional principal component analysis (2DPCA) and two-dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE), which is sensitive to outliers. To overcome this problem, in this paper we propose a new robust 2DSVD method based on the kernel mean p power error loss (KMPE-2DSVD). Different from the MSE and the correntropy based ones which are second order statistics based measurements, the KMPE-2DSVD is based on the non-second order statistics in the kernel space, and thus is more flexible in controlling the representation error. Experimental results show that the proposed method significantly improves the accuracy of facial image clustering.
    View less >
    Conference Title
    Proceedings - International Conference on Image Processing, ICIP
    Volume
    2019-September
    DOI
    https://doi.org/10.1109/ICIP.2019.8803437
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/392522
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

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