L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
Matey Neykov, Jun S. Liu, Tianxi Cai; 17(87):1−37, 2016.
Abstract
It is known that for a certain class of single index models (SIMs) $Y = f(X_{p \times 1}^\top\beta_0, \varepsilon)$, support recovery is impossible when $X \sim \mathcal{N}(0, I_{p \times p})$ and a model complexity adjusted sample size is below a critical threshold. Recently, optimal algorithms based on Sliced Inverse Regression (SIR) were suggested. These algorithms work provably under the assumption that the design $X$ comes from an i.i.d. Gaussian distribution. In the present paper we analyze algorithms based on covariance screening and least squares with $L_1$ penalization (i.e. LASSO) and demonstrate that they can also enjoy optimal (up to a scalar) rescaled sample size in terms of support recovery, albeit under slightly different assumptions on $f$ and $\varepsilon$ compared to the SIR based algorithms. Furthermore, we show more generally, that LASSO succeeds in recovering the signed support of $\beta_0$ if $X \sim \mathcal{N}(0, \Sigma)$, and the covariance $\Sigma$ satisfies the irrepresentable condition. Our work extends existing results on the support recovery of LASSO for the linear model, to a more general class of SIMs.
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