Feature Generation Method by Geometrical Interpretation of Fisher Linear Discriminant Analysis

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  • Oyama Tadahiro
    Department of Information & Science Intelligent Systems, The University of Tokushima
  • Matsumura Yuji
    Department of Information & Science Intelligent Systems, The University of Tokushima
  • Karungaru Stephen Githinji
    Department of Information & Science Intelligent Systems, The University of Tokushima
  • Fukumi Minoru
    Department of Information & Science Intelligent Systems, The University of Tokushima

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This paper presents a new algorithm for feature generation, which is derived based on geometrical interpretation of the fisher linear discriminant analysis (FLDA). This algorithm (Simple-FLDA) is an approximation algorithm that calculates eigenvectors sequentially by an easy iterative calculation by expressing the maximization of variance between classes and minimization of variance in each class without the use of matrix calculation. We carry out computer simulations about recognition of wrist motion patterns by EMG measured from wrist and personal authentications that use face images to verify the effectiveness of this technique. The result was compared with the result of principal component analysis (Simple-PCA).

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