The Dantzig Selector is a statistical method used for sparse recovery that aims to find the most relevant features from high-dimensional data while minimizing a specific loss function. This approach combines L1 regularization with a constraint on the maximum absolute correlation between the observed data and the selected features, making it effective in situations where the number of features far exceeds the number of observations.
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