Lasso-type recovery of sparse representations for high-dimensional data
The Lasso is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables $p_n$ is potentially much larger than the number of samples $n$. However, it was recently discovered that the sparsity pattern of the Lasso estimator can...
| Главные авторы: | , |
|---|---|
| Формат: | Journal article |
| Язык: | English |
| Опубликовано: |
2008
|