Greedy forward regression for variable screening

Faithful Variable Screening for High-dimensional Convex Regression By

We study the problem of variable selection in convex nonparametric regression. Under the assumption that the true regression function is convex and sparse, we develop a screening procedure to select a subset of variables that contains the relevant variables. Our approach is a two-stage quadratic programming method that estimates a sum of one-dimensional convex functions, followed by one-dimensi...

A Gradient-Based Forward Greedy Algorithm for Space Gaussian Process Regression

In this chaper, we present a gradient-based forward greedy method for sparse approximation of Bayesian Gaussian Process Regression (GPR) model. Different from previous work, which is mostly based on various basis vector selection strategies, we propose to construct instead of select a new basis vector at each iterative step. This idea was motivated from the well-known gradient boosting approach...

A Gradient-based Forward Greedy Algorithm for Sparse Gaussian Process Regression

In this chaper, we present a gradient-based forward greedy method for sparse approximation of Bayesian Gaussian Process Regression (GPR) model. Different from previous work, which is mostly based on various basis vector selection strategies, we propose to construct instead of select a new basis vector at each iterative step. This idea was motivated from the well-known gradient boosting approach...

Forward - Backward greedy algorithms for signal demixing

Signal demixing arises in many applications. Common among these are the separation of sparse and low rank components in image and video processing, sparse and group sparse models in multitask learning and spikes and sinusoids in source separation problems. For specific problems of interest, many methods exist to perform recovery, but an approach that generalizes to arbitrary notions of simplici...

Some Greedy Algorithms for Sparse Nonlinear Regression