Spectral Path Regression: Directional Chebyshev Harmonics for Interpretable Tabular Learning

ArXi:2604.04091v1 Announce Type: new Classical approximation bases such as Chebyshe polynomials provide principled and interpretable representations, but their multivariate tensor-product constructions scale exponentially with dimension and impose axis-aligned structure that is poorly matched to real tabular data. We address this by replacing tensorised oscillations with directional harmonic modes of the form $\cos(\mathbf{m}^{\top}\arccos(\mathbf{x}))$, which organise multivariate structure by direction in angular space rather than by coordinate index.