Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance.

ABSTRACT: Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative imp

SUBMITTER: Wild R 

PROVIDER: S-EPMC11696465 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature