Researchers have introduced a novel framework for feature selection, dubbed Noise-Based Spectral Embedding (NBSE), which leverages physics-informed principles to identify informative features in high-dimensional data without relying on greedy search methods. By constructing a sparse similarity graph, NBSE determines the critical inverse temperature, known as the Nishimori temperature, at which the Bethe Hessian becomes singular. The corresponding smallest eigenvector captures the dominant features, enabling efficient selection. This approach has significant implications for machine learning and data analysis, particularly in scenarios where high-dimensional data poses a challenge1. The ability to select informative features without greedy search can lead to more accurate and efficient models. This matters to practitioners because it can enhance the performance of machine learning models in various applications, ultimately leading to better decision-making and improved outcomes.