An Empirical Comparison of Spectral Learning Methods for Classification

In this paper, we explore the problem of how to learn spectral (e.g., Fourier) models for classification problems. Specifically, we consider two sub-problems of spectral learning: (1) how to select the basis functions that will be included in the model and (2) how to assign coefficients to the selected basis functions. Interestingly, empirical results suggest that the most commonly used approach does not perform as well in practice as other approaches, while a method for assigning coefficients based on finding an optimal linear combination of low-order basis functions usually outperforms other approaches.