OPTIMAL PARAMETER SETTING OF SINGLE AND MULTI-TASK LASSO
SuHuiting
2019
This thesis considers the problem of feature selection when the number of predictors
is larger than the number of samples. The performance of supersaturated design
(SSD) working with least absolute shrinkage and selection operator (LASSO) is studied
in this setting. In order to achieve higher feature selection correctness, self-voting
LASSO is implemented to select the tuning parameter while approximately optimize
the probability of achieving Sign Correctness. Furthermore, we derive the probability
of achieving Direction Correctness, and extend the self-voting LASSO to multi-task
self-voting LASSO, which has a group screening effect for multiple tasks.