1 citations · 1 across the 5 of their papers we have counts for
5 papers
Tuning parameter selection for the adaptive nuclear norm regularized trace regression
Pan Shang, Lingchen Kong, Yiting Ma
Regularized models have been applied in lots of areas, with high-dimensional data sets being popular. Because tuning parameter decides the theoretical performance and computational…
Safe Feature Identification Rule for Fused Lasso by An Extra Dual Variable
Pan Shang, Huangyue Chen, Lingchen Kong
Fused Lasso was proposed to characterize the sparsity of the coefficients and the sparsity of their successive differences for the linear regression. Due to its wide applications,…
Safe subspace screening for the adaptive nuclear norm regularized trace regression
Pan Shang, Lingchen Kong
Matrix form data sets arise in many areas, so there are lots of works about the matrix regression models. One special model of these models is the adaptive nuclear norm regularized…
Regularization parameter selection for low rank matrix recovery
Pan Shang, Lingchen Kong
Low rank matrix recovery is the focus of many applications, but it is a NP-hard problem. A popular way to deal with this problem is to solve its convex relaxation, the nuclear norm…
Tuning parameter selection rules for nuclear norm regularized multivariate linear regression
Pan Shang, Lingchen Kong
We consider the tuning parameter selection rules for nuclear norm regularized multivariate linear regression (NMLR) in high-dimensional setting. High-dimensional multivariate linea…