Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 52, Issue 1, Pages 155-173Publisher
ELSEVIER
DOI: 10.1016/j.csda.2006.11.006
Keywords
nonnegative matrix factorization; text mining; spectral data analysis; email surveillance; conjugate gradient; constrained least squares
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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. (c) 2006 Elsevier B.V. All rights reserved.
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