W. J. Krzanowski, P. Jonathan, W. V. McCarthy and M. R. Thomas, “Discriminant Analysis with Singular Covariance Matrices Methods and Applications to Spectroscopic Data,” Applied Statistics, Vol. 44, No. 1, 1995, pp. 101-115. doi10.2307/2986198 - References

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W. J. Krzanowski, P. Jonathan, W. V. McCarthy and M. R. Thomas, “Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data,” Applied Statistics, Vol. 44, No. 1, 1995, pp. 101-115. doi:10.2307/2986198

has been cited by the following article:

TITLE: Face Recognition Systems Using Relevance Weighted Two Dimensional Linear Discriminant Analysis Algorithm

AUTHORS: Hythem Ahmed, Jedra Mohamed, Zahid Noureddine

KEYWORDS: LDA; PCA; 2DLDA; RW2DLDA; Extraction; Face Recognition; Small Sample Size

JOURNAL NAME: Journal of Signal and Information Processing, Vol.3 No.1, February 29, 2012

ABSTRACT: Low-dimensional feature representation with enhanced discriminatory power of paramount importance to face recognition systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in face recognition tasks. In this paper, we propose a new technique coined Relevance-Weighted Two Dimensional Linear Discriminant Analysis (RW2DLDA). Its over comes the singularity problem implicitly, while achieving efficiency. Moreover, a weight discriminant hyper plane is used in the between class scatter matrix, and RW method is used in the within class scatter matrix to weigh the information to resolve confusable data in these classes. Experiments on two well known facial databases show the effectiveness of the proposed method. Comparisons with other LDA-based methods show that our method improves the LDA classification performance.