<i>L</i><sub>1</sub>-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs.
basic_science · Level V
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Abstract
It is known that for a certain class of single index models (SIMs) [Formula: see text], support recovery is impossible when <b><i>X</i></b> ~ 𝒩(0, 𝕀 <i><sub>p</sub></i><sub>×</sub><i><sub>p</sub></i> ) and a <i>model complexity adjusted sample size</i> 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 <b><i>X</i></b> comes from an i.i.d. Gaussian distribution. In the present paper we analyze algorithms based on covariance screening and least squares with <i>L</i><sub>1</sub> 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 <i>f</i> and <i>ε</i> compared to the SIR based algorithms. Furthermore, we show more generally, that LASSO succeeds in recovering the signed support of <b><i>β</i></b><sub>0</sub> if <b><i>X</i></b> ~ 𝒩 (0, <b>Σ</b>), and the covariance <b>Σ</b> 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.