Data-Driven Variational Basis Learning Beyond Neural Networks: A Non-Neural Framework for Adaptive Basis Discovery

This manuscript introduces Data Driven Variational Basis Learning (DVBL), a novel non-neural framework for learning data-adaptive basis functions directly from high-dimensional data. It provides an explicit, interpretable, and mathematically transparent alternative to neural networks for representation learning, addressing their limitations in control and transparency.