Many classification models assign a real-valued score to each object and apply a threshold to determine class membership. While a variety of well-established methods exist for constructing such scores, the use of spectral techniques has received little attention in this context. In this paper, we explore a novel classification approach that treats the label function of binary training data as a signal over the feature space. Using the Discrete Cosine Transform (DCT), we approximate this signal on a sparse grid and reconstruct a smooth decision function whose values are subjected to a fixed threshold. This formulation inherently emphasizes low-frequency components, which promotes smoothness and potentially improves generalization. We discuss the theoretical motivation, implementation challenges, and present experiments that suggest spectral methods may offer an alternative perspective on binary classification.