Sparse Nonparametric Regression With Regularized Tensor Product Kernel.

ABSTRACT: With growing interest to use black-box machine learning for complex data with many feature variables, it is critical to obtain a prediction model that only depends on a small set of features to maximize generalizability. Therefore, feature selection remains to be an important and challenging problem in modern applications. Most of existing methods for feature selection are based on either parametric or semiparametric models, so the resulting performance can severely suffer from model misspecification when high-order nonlinear interactions among the features are present. A very limited number of approaches for nonparametric feature selection were proposed, but they are computationally intensive and may not even converge. In this paper, we propose a novel and computationally efficient approa

SUBMITTER: Yu H 

PROVIDER: S-EPMC8021131 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature