Spectral Path Regression: Directional Chebyshev Harmonics for Interpretable Tabular Learning

arXiv:2604.04091v1 Announce Type: new Abstract: Classical approximation bases such as Chebyshev polynomials provide principled and interpretable representations, but their multivariate tensor-product constructions scale exponentially with dimension and impose axis-aligned structure that is poorly ma