L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs.
J Mach Learn Res
; 17(1): 2976-3012, 2016 May.
Article
en En
| MEDLINE
| ID: mdl-28503101
ABSTRACT
It is known that for a certain class of single index models (SIMs) [Formula see text], support recovery is impossible when X ~ ð©(0, ð p×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 L1 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 ε compared to the SIR based algorithms. Furthermore, we show more generally, that LASSO succeeds in recovering the signed support of ß0 if X ~ ð© (0, Σ), and the covariance Σ 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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1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
J Mach Learn Res
Año:
2016
Tipo del documento:
Article
País de afiliación:
Estados Unidos