2016
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
ICML 2016poster
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individual…