Sparse PCA from Sparse Linear Regression
This provides a practical framework for deriving SPCA algorithms from existing SLR methods, benefiting researchers and practitioners in high-dimensional statistics, though it is incremental in linking two established problems.
The paper tackles the problem of connecting Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression (SLR) by showing how to transform an SLR solver into an SPCA algorithm, achieving near state-of-the-art guarantees for testing and support recovery in the single spiked covariance model.
Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression (SLR) have a wide range of applications and have attracted a tremendous amount of attention in the last two decades as canonical examples of statistical problems in high dimension. A variety of algorithms have been proposed for both SPCA and SLR, but an explicit connection between the two had not been made. We show how to efficiently transform a black-box solver for SLR into an algorithm for SPCA: assuming the SLR solver satisfies prediction error guarantees achieved by existing efficient algorithms such as those based on the Lasso, the SPCA algorithm derived from it achieves near state of the art guarantees for testing and for support recovery for the single spiked covariance model as obtained by the current best polynomialtime algorithms. Our reduction not only highlights the inherent similarity between the two problems, but also, from a practical standpoint, allows one to obtain a collection of algorithms for SPCA directly from known algorithms for SLR. We provide experimental results on simulated data comparing our proposed framework to other algorithms for SPCA.