Discriminative Non-negative Matrix Factorization Model with Local Manifold Preservation
A novel NMF-based framework that incorporates supervised information to enhance class discriminability in the learned latent space is proposed and explicitly preserves the local geometric structure of the data during factorization, thereby improving the fidelity of the learned representation.