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Discriminant And Sparsity Based Least Squares Regression With L1 Regularization For Feature Representation
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Discriminant And Sparsity Based Least Squares Regression With L1 Regularization For Feature Representation
Least squares regression (LSR) has two main issues that greatly limits the improvement of performance: 1) The target matrix is too rigid to learn a discriminative projection matrix leading to a large regression error; 2) the underlying geometric structure
Least squares regression (LSR) has two main issues that greatly limits the improvement of performance: 1) The target matrix is too rigid to learn a discriminative projection matrix leading to a large regression error; 2) the underlying geometric structure
