Algorithms and applications for approximate nonnegative matrix factorization

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Algorithms and applications for approximate nonnegative matrix factorization (2006)

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by Michael W. Berry , Murray Browne , Amy N. Langville , V. Paul Pauca , Robert J. Plemmons

Venue:	Computational Statistics and Data Analysis
Citations:	196 - 8 self

BibTeX

@INPROCEEDINGS{Berry06algorithmsand,
    author = {Michael W. Berry and Murray Browne and Amy N. Langville and V. Paul Pauca and Robert J. Plemmons},
    title = {Algorithms and applications for approximate nonnegative matrix factorization},
    booktitle = {Computational Statistics and Data Analysis},
    year = {2006},
    pages = {155--173}
}

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Abstract

In this paper we discuss 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. 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 datasets. Key words: nonnegative matrix factorization, text mining, spectral data analysis, email surveillance, conjugate gradient, constrained least squares.

Keyphrases

approximate nonnegative matrix factorization text mining spectral data analysis nonnegative matrix factorization convergence property future work nmf output smoothness constraint feature extraction conjugate gradient time-varying datasets key word low-rank approximate nonnegative matrix factorization nonnegative matrix factor nmf algorithm hybrid method specific context email surveillance

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