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• W2896712872 abstract "Abstract Feature selection (FS) is an important issue in the field of pattern recognition and machine learning. Linear regression (LR) and its variants have been widely used for classification problems. In this paper, we propose a novel feature selection method, i.e., l2,1-norm minimization based negative label relaxation linear regression for feature selection (NLRL21-FS). The core idea of our method is that a technique called negative label relaxation is integrated into LR model for classification, and l2,1-norm regularization constraint is imposed on transformation matrix of the modified LR model to achieve row sparsity. Thus we incorporate feature selection into the training process of the classifier. An efficient algorithm is devised to solve the objective function and analysis of convergence of the proposed algorithm is presented. Our feature selection method is more efficient due to joint relaxing class labels and l2,1-norm regularization. Three groups of experiments on 6 benchmark data sets of different type demonstrate the effectiveness of the proposed feature selection method." @default.
• W2896712872 created "2018-10-26" @default.
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• W2896712872 date "2018-12-01" @default.
• W2896712872 modified "2023-09-23" @default.
• W2896712872 title "l2,1-norm minimization based negative label relaxation linear regression for feature selection" @default.
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• W2896712872 doi "https://doi.org/10.1016/j.patrec.2018.10.016" @default.
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