We present a new computational approach to approximating a large, noisy data table by a low-rank matrix with sparse singular vectors. The approximation is obtained from thresholded subspace iterations ...
The Lasso is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables $p_{n}$ is ...
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