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Two-dimensional principal component analysis (2DPCA) serves as an efficient approach for both dimensionality reduction and high-quality reconstruction. However, conventional 2DPCA method is sensitive to the outliers such that associated results could be compromised. To strengthen the robustness of conventional 2DPCA method, we try to propose a novel robust two-dimensional principal component analysis...
Feature selection and feature transformation, the two main ways to reduce dimensionality, are often presented separately. In this paper, a feature selection method is proposed by combining the popular transformation-based dimensionality reduction method linear discriminant analysis (LDA) and sparsity regularization. We impose row sparsity on the transformation matrix of LDA through ${\ell }_{2,1}$ ...
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