# On Cross-validation for Sparse Reduced Rank Regression

## TL;DR

This paper investigates cross-validation strategies for sparse reduced rank regression in high-dimensional settings, proposing new criteria and calibration methods to improve model selection and estimation accuracy.

## Contribution

It introduces a novel approach to cross-validation that focuses on projection-selection patterns and develops scale-free information criteria for better regularization parameter tuning.

## Key findings

- Proposes a new class of scale-free information criteria.
- Introduces a scale-free calibration method for minimax optimal error rate.
- Experimental results demonstrate the effectiveness of the proposed methods.

## Abstract

In high-dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received a lot of 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 the best coefficient estimate. Our in-sample error studies in jointly sparse and rank-deficient models lead to a new class of information criteria with four scale-free forms to bypass the estimation of the noise level. By use of an identity, we propose a novel scale-free calibration to help cross-validation achieve the minimax optimal error rate non-asymptotically. Experiments support the efficacy of the proposed methods.