IEEE Trans Image Process - Linear discriminant analysis based on L1-norm maximization.

Tópicos

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Resumo

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.

Resumo Limpo

linear discrimin analysi lda wellknown dimension reduct techniqu wide use mani purpos howev convent lda sensit outlier object function base distanc criterion use lnorm paper propos simpl effect robust lda version base lnorm maxim learn set local optim project vector maxim ratio lnormbas betweenclass dispers lnormbas withinclass dispers propos method theoret prove feasibl robust outlier overcom singular problem withinclass scatter matrix convent lda experi artifici dataset standard classif dataset three popular imag databas demonstr efficaci propos method

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