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• W2017288758 abstract "The development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis are presented. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the modification of NMF algorithms for large-scale and time-varying data sets." @default.
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• W2017288758 date "2007-09-01" @default.
• W2017288758 modified "2023-10-09" @default.
• W2017288758 title "Algorithms and applications for approximate nonnegative matrix factorization" @default.
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• W2017288758 doi "https://doi.org/10.1016/j.csda.2006.11.006" @default.
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