Minimum Volume Constraint with Perturbation for Non-negative Matrix Factorization

Abstract

Nonnegative matrix factorization (NMF) has been applied in hyperspectral unmixing. The nonconvexity of the NMF’s cost function leads to solutions that are only locally optimal. Adding regularized terms to the NMF helps improve the solutions. In this study, we proposed a regularized NMF model, the regularized tern is the minimum volume constraint with perturbation. The NMF model is solved with multiplicative updated rules. Numerical results verified that adding a disturbance term to the minimum volume constraint effectively improves the spectral curve’s local accuracy while maintaining the original model’s advantages.