Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination
Methodology
2024-11-04 v1
Abstract
This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smoothness of each feature while enabling precise determination of whether a feature's contribution to the response is zero, linear, or nonlinear. Further, an efficient coordinate descent algorithm is introduced to implement VARD. Empirical evaluations on simulated and real-world data underscore VARD's superiority over alternative variable selection methods for additive models.
Cite
@article{arxiv.2411.00256,
title = {Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination},
author = {Zihe Liu and Diptarka Saha and Feng Liang},
journal= {arXiv preprint arXiv:2411.00256},
year = {2024}
}