On ℓ_1-regularized estimation for nonlinear models that have sparse underlying linear structures

11/25/2009
by   Zhiyi Chi, et al.
0

In a recent work (arXiv:0910.2517), for nonlinear models with sparse underlying linear structures, we studied the error bounds of ℓ_0-regularized estimation. In this note, we show that ℓ_1-regularized estimation in some important cases can achieve the same order of error bounds as those in the aforementioned work.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/14/2009

L_0 regularized estimation for nonlinear models that have sparse underlying linear structures

We study the estimation of β for the nonlinear model y = f(Xβ) + ϵ when ...
research
04/22/2022

Regularized randomized iterative algorithms for factorized linear systems

Randomized iterative algorithms for solving a factorized linear system, ...
research
03/16/2023

Error analysis of regularized trigonometric linear regression with unbounded sampling: a statistical learning viewpoint

The effectiveness of non-parametric, kernel-based methods for function e...
research
09/28/2022

Generalized Kernel Regularized Least Squares

Kernel Regularized Least Squares (KRLS) is a popular method for flexibly...
research
02/05/2019

Uniform concentration and symmetrization for weak interactions

The method to derive uniform bounds with Gaussian and Rademacher complex...
research
11/01/2021

Concentration bounds for the extremal variogram

In extreme value theory, the extremal variogram is a summary of the tail...
research
05/18/2021

Sparsity Prior Regularized Q-learning for Sparse Action Tasks

In many decision-making tasks, some specific actions are limited in thei...

Please sign up or login with your details

Forgot password? Click here to reset