We propose graph consistency regularization, a method that improves classification by aligning feature and prediction structures to suppress inter-class noise and strengthen intra-class cohesion.

We revisit the orbital minimization method (OMM) from computational chemistry and demonstrate its utility as a simple, principled, and scalable approach for modern machine learning.