
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.


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algorithms-for-handling-noisy-data-with-negative-values-in-non-negative-matrix-factorization


Algorithms for Handling Noisy Data with Negative Values in Non-Negative Matrix Factorization
