.png){kind=icon}
Return
CONSISTENT PARAMETER ESTIMATION FOR LASSO AND APPROXIMATE MESSAGE PASSING
.png){kind=icon}
OA
AI
DOI：10.1214/17-AOS1544.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper studies the optimal tuning of the regularization parameter in LASSO or the threshold parameters in approximate message passing (AMP). Considering a model in which the design matrix and noise are zero-mean i.i.d. Gaussian, we propose a data-driven approach for estimating the regularization parameter of LASSO and the threshold parameters in AMP. Our estimates are consistent, that is, they converge to their asymptotically optimal values in probability as n, the number of observations, and p, the ambient dimension of the sparse vector, grow to infinity, while n/p converges to a fixed number delta. As a byproduct of our analysis, we will shed light on the asymptotic properties of the solution paths of LASSO and AMP.
Keywords:
LASSO
estimation
sparsity
approximate message passing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.7
Papers:
2.8K
Citations:
2.9W
Organization
Cited Papers
ShareSaveShareSaveModelling of DNA Complexes with Distamycin Analogues Using an ab initio Continuum Solvent Model
PREAI
ShareSaveShareSaveShareSaveLASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSave
View more