Neural nonnegative matrix factorization for hierarchical multilayer topic modeling
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43670_2023_Article_77.pdf
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Author(s)
Haddock, Jamie
Will, Tyler
Vendrow, Joshua
Zhang, Runyu
Molitor, Denali
Needell, Deanna
Gao, Mengdi
Sadovnik, Eli
Date Issued
December 19, 2023
Publisher
Springer International Publishing
Citation
Sampling Theory, Signal Processing, and Data Analysis. 2023 Dec 19;22(1):4
Version
Final published version
Abstract
We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a wide variety of fields, such as document classification, image processing, and bioinformatics. Neural NMF recursively applies NMF in layers to discover overarching topics encompassing the lower-level features. We derive a backpropagation optimization scheme that allows us to frame hierarchical NMF as a neural network. We test Neural NMF on a synthetic hierarchical dataset, the 20 Newsgroups dataset, and the MyLymeData symptoms dataset. Numerical results demonstrate that Neural NMF outperforms other hierarchical NMF methods on these data sets and offers better learned hierarchical structure and interpretability of topics.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s43670-023-00077-3
