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This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an...
High speed security and defense applications demand a quick decision for face recognition which requires a computationally time-efficient algorithm. These algorithms are primarily used to generate egien values. The generation of eigen values by employing decomposition method normally provides solution in O(n3) time whereas an orthogonalizational process, called fast principal component analysis (PCA)...
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