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Feature extraction plays an important role in analyzing data with multivariate features. Linear discriminant analysis based on L1-norm (LDA-L1) is a recently developed technique for enhancing the robustness of the classic LDA against outliers. However, LDA-L1 employs a greedy strategy to find all the discriminant vectors, which may lead to suboptimal solution. To address this issue, we develop a novel...
In this paper, we develop a novel dimensionality reduction (DR) framework coined complete large margin linear discriminant analysis (CLMLDA). Inspired by several recently proposed DR methods, CLMLDA constructs two mathematical programming models by maximizing the minimum distance between each class center and the total class center respectively in the null space of within-class scatter matrix and...
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