Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection
This work addresses variable selection for high-dimensional data analysis, offering incremental improvements to an existing method for better robustness and sparsity handling.
The authors tackled the problem of selecting key variables from high-dimensional data by enhancing the Variational Garrote method with automatic differentiation, resulting in more consistent and robust variable selection than Ridge and LASSO, especially in highly sparse regimes, with a sharp transition point that helps estimate the correct number of relevant variables.
Selecting key variables from high-dimensional data is increasingly important in the era of big data. Sparse regression serves as a powerful tool for this purpose by promoting model simplicity and explainability. In this work, we revisit a valuable yet underutilized method, the statistical physics-based Variational Garrote (VG), which introduces explicit feature selection spin variables and leverages variational inference to derive a tractable loss function. We enhance VG by incorporating modern automatic differentiation techniques, enabling scalable and efficient optimization. We evaluate VG on both fully controllable synthetic datasets and complex real-world datasets. Our results demonstrate that VG performs especially well in highly sparse regimes, offering more consistent and robust variable selection than Ridge and LASSO regression across varying levels of sparsity. We also uncover a sharp transition: as superfluous variables are admitted, generalization degrades abruptly and the uncertainty of the selection variables increases. This transition point provides a practical signal for estimating the correct number of relevant variables, an insight we successfully apply to identify key predictors in real-world data. We expect that VG offers strong potential for sparse modeling across a wide range of applications, including compressed sensing and model pruning in machine learning.