Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination

Zihe Liu; Diptarka Saha; Feng Liang

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arXiv · 2411.00256

Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination

Zihe Liu·Diptarka Saha·Feng Liang

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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.

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Zihe Liu, Diptarka Saha, Feng Liang. 2024-10-31. Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination. https://arxiv.org/abs/2411.00256

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