Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification

ArXi:2603.29917v1 Announce Type: new This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybrid feature representation. Inspired by our previous work on brain tumor classification, the proposed approach operates in a feature space to improve the robustness to noise and adversarial attacks. First, the input images are converted into tight, interpretable exemplification using Nonnegative Matrix Factorization.