… improve the robustness to noise, we introduce a sparse error … of the original data in the
discriminant subspace, the proposed … a novel and robust sparse discriminative feature extraction …

… CONCLUSION This paper proposed a robust discriminant analysis cri… sparse version RSLDA
by considering an extra sparse regularization term. This makes our methods more robust to …

… Classical linear discriminant analysis (LDA) or Fisher’s LDA addresses the classification …
can be mapped into the most discriminative low-dimensional subspace (Fisher, 1936; Hastie et …

… the linear discriminant analysis (LDA) (part b), sparse LDA (part c) and robust sparse LDA
(… Obviously, the projected matrices obtained by sparse LDA and robust sparse LDA present a …

… Discriminative methods pursue a direct … , linear discriminant analysis (LDA) aims to find the
mapping that reduces the input dimensionality, while preserving the most class discriminatory …

… Sparse discriminant analysis is based on the optimal scoring interpretation of linear
discriminant analysis, and can be extended to perform sparse discrimination via mixtures of …

Linear Discriminant Analysis (LDA) is an efficient image feature extraction technique by
supervised dimensionality reduction. In this paper, we extend LDA to Structured Sparse LDA (…

… efficient and robust sparse linear discriminant analysis (… L2,p-norm can bring higher
robustness and better accuracy. … , which further enhances the robustness in different scenarios. In …

… of robust linear discriminant analysis for dimensionality reduction which joints L2;1-norm on
objective function … such as multivariate linear regression, PCA and sparse coding and exploit …

… Linear discriminant analysis (LDA) is a well-known supervised method that … robust linear
discriminant analysis (GRLDA) method to tackle this disadvantage and improve the robustness. …