Lasso-type recovery of sparse representations for high-dimensional data

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...

書誌詳細
主要な著者:	Meinshausen, N, Yu, B
フォーマット:	Journal article
言語:	English
出版事項:	2008

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