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Linear Discriminant Analysis Based on L1-Norm Maximization

Fujin Zhong ; Jiashu Zhang

IEEE Transactions on Image Processing, August 2013, Vol.22(8), pp.3018-3027 [Periódico revisado por pares]

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  • Título:
    Linear Discriminant Analysis Based on L1-Norm Maximization
  • Autor: Fujin Zhong ; Jiashu Zhang
  • Assuntos: Vectors ; Linear Programming ; Principal Component Analysis ; Nickel ; Robustness ; Optimization ; Training ; Linear Discriminant Analysis (Lda) ; L1-Norm ; L2-Norm ; Optimization ; Outliers ; Engineering ; Applied Sciences
  • É parte de: IEEE Transactions on Image Processing, August 2013, Vol.22(8), pp.3018-3027
  • Descrição: Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method.
  • Idioma: Inglês

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