Unlabeled data is a key component of modern machine learning. In general, the roleof unlabeled data is to impose a form of smoothness, usually from the similarityinformation encoded in a base kernel, such as the ϵ-neighbor kernel or the adjacencymatrix of a graph. This work revisits the classical idea of spectrally transformedkernel regression (STKR), and provides a new class of general and scalable STKRestimators able to leverage unlabeled data. Intuitively, via spectral transformation,STKR exploits the data distribution for which unlabeled data can provide additionalinformation. First, we show that STKR is a principled and general approach,by characterizing a universal type of “target smoothness”, and proving that anysufficiently smooth function can be learned by STKR. Second, we provide scalableSTKR implementations for the inductive setting and a general transformationfunction, while prior work is mostly limited to the transductive setting. Third, wederive statistical guarantees for two scenarios: STKR with a known polynomialtransformation, and STKR with kernel PCA when the transformation is unknown.Overall, we believe that this work helps deepen our understanding of how to workwith unlabeled data, and its generality makes it easier to inspire new methods.