• DocumentCode
    326977
  • Title

    Linear feature extraction for multiclass problems

  • Author

    Hsieh, Pi-Fuei ; Landgrebe, David

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
  • Volume
    4
  • fYear
    1998
  • fDate
    6-10 Jul 1998
  • Firstpage
    2050
  • Abstract
    Motivated by the need for a fast and effective feature extraction method for multiclass problems, a feature extraction method is developed to satisfy two requirements: (1) perform on a class-statistics basis (2) use discriminant information about covariance-difference as well as mean-difference. Experiments show that the new feature extraction method has fulfilled the requirements when the number of training samples is large. Experiments with a small number of training samples were also conducted for showing the limitation of feature extraction
  • Keywords
    feature extraction; geophysical signal processing; image classification; remote sensing; class-statistics basis; covariance-difference; discriminant information; feature extraction; linear feature extraction; mean-difference; multiclass problems; training samples; Data analysis; Data mining; Feature extraction; Gaussian distribution; Hyperspectral imaging; Information analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7803-4403-0
  • Type

    conf

  • DOI
    10.1109/IGARSS.1998.703737
  • Filename
    703737