On Cross-Validation for Sparse Reduced Rank Regression
稀疏降秩回归中的交叉验证
Summary In high dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received much attention recently. To provide a proper amount of shrinkage, it is typical to use a grid search and a model comparison criterion to find the optimal regularization parameters. However, we show that fixing the parameters across all folds may result in an inconsistency issue, and it is more appropriate to cross-validate projection–selection patterns to obtain…