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Nonnegative Matrix Factorization Using Nonnegative Polynomial Approximations
IEEE Signal Processing Letters, 2017Nonnegative matrix factorization is a key tool in many data analysis applications such as feature extraction, compression, and noise filtering. Many existing algorithms impose additional constraints to take into account prior knowledge and to improve the physical interpretation.
Otto Debals +2 more
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Weighted nonnegative matrix factorization
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. A large body of past work on NMF has focused on the case where the data matrix is complete.
Yongsun Kim, Seungjin Choi
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Nonnegative matrix factorization with matrix exponentiation
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Nonnegative matrix factorization (NMF) has been successfully applied to different domains as a technique able to find part-based linear representations for nonnegative data. However, when extra constraints are incorporated into NMF, simple gradient descent optimization can be inefficient for high-dimensional problems, due to the overhead to enforce the
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Kullback-Leibler Divergence for Nonnegative for Nonnegative Matrix Factorization
2011Zhang, He +4 more
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