Linear Discriminant Analysis Based on L1-Norm Maximization
In: IEEE transactions on image processing, Jg. 22 (2013), Heft 7-8, S. 3018-3027
Online
academicJournal
- print, 34 ref
Zugriff:
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.
Titel: |
Linear Discriminant Analysis Based on L1-Norm Maximization
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Autor/in / Beteiligte Person: | FUJIN, ZHONG ; JIASHU, ZHANG |
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Zeitschrift: | IEEE transactions on image processing, Jg. 22 (2013), Heft 7-8, S. 3018-3027 |
Veröffentlichung: | New York, NY: Institute of Electrical and Electronics Engineers, 2013 |
Medientyp: | academicJournal |
Umfang: | print, 34 ref |
ISSN: | 1057-7149 (print) |
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