Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
This work provides an incremental improvement for researchers and practitioners in statistics and machine learning dealing with high-dimensional covariance estimation.
The paper tackles the problem of estimating sparse precision matrices in high-dimensional settings by proposing a novel Sparse Column-wise Inverse Operator method that addresses fast computation and adaptivity, achieving established convergence rates under various matrix norms and demonstrating favorable performance on real datasets like HIV brain tissue and ADHD fMRI data.
This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive procedure based on cross validation, and establish its convergence rate under the Frobenius norm. The convergence rates under other matrix norms are also established. This method also enjoys the advantage of fast computation for large-scale problems, via a coordinate descent algorithm. Numerical merits are illustrated using both simulated and real datasets. In particular, it performs favorably on an HIV brain tissue dataset and an ADHD resting-state fMRI dataset.