Algorithms for Handling Noisy Data with Negative Values in Non-Negative Matrix Factorization
This paper presents two novel algorithms, Shift-NMF and Nearly-NMF, that can handle noisy data with negative values in non-negative matrix factorization (NMF) while maintaining the non-negativity constraints on the templates and coefficients.