Feature learning is a crucial element for the performance of machine learning models. Recently, the exploration of feature learning in the context of kernel methods has led to the introduction of Recursive Feature Machines (RFMs). In this work, we connect diagonal RFMs to Automatic Relevance Determination (ARD) from the Gaussian process literature. We demonstrate that diagonal RFMs, similar to ARD, serve as a weighted covariate selection technique. However, they are trained using different paradigms: RFMs use recursive iterations of the so-called Average Gradient Outer Product, while ARD employs maximum likelihood estimation. Our experiments show that while the learned features in both models correlate highly across various tabular datasets, this correlation is lower for other datasets. Furthermore, we demonstrate that the RFM effectively captures correlation between covariates, and we present instances where the RFM outperforms both ARD and diagonal RFM.