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ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data by Yang, Songshan; Wen, Jiawei; Zhan, Xiang; Kifer, Daniel is a scholarly article available to read on EtoBox.

What is ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data about?

The L1 regularization (Lasso) has proven to be a versatile tool to select relevant features and estimate the model coefficients simultaneously and has been widely used in many research areas such as genomes studies, finance, and biomedical imaging. Despite its popularity, it is very challenging to guarantee the feature selection consistency of Lasso especially when the dimension of the data is huge. One way to improve the feature selection consistency is to select an ideal tuning parameter. Traditional tuning criteria mainly focus on minimizing the estimated prediction error or maximizing the posterior model probability, such as cross-validation and BIC, which may either be time-consuming or fail to control the false discovery rate (FDR) when the number of features is extremely large. The other way is to introduce pseudo-features to learn the importance of the original ones. Recently, the Knockoff filter is proposed to control the FDR when performing feature selection. However, its performance is sensitive to the choice of the expected FDR threshold. Motivated by these ideas, we propose a new method using pseudo-features to obtain an ideal tuning parameter. In particular, we presen

Author

Yang, Songshan; Wen, Jiawei; Zhan, Xiang; Kifer, Daniel

Published

2018

Language

EN