Neural nonnegative matrix factorization for hierarchical multilayer topic modeling

Author:

Haddock JamieORCID,Will Tyler,Vendrow Joshua,Zhang Runyu,Molitor Denali,Needell Deanna,Gao Mengdi,Sadovnik Eli

Abstract

AbstractWe 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.

Funder

Division of Mathematical Sciences

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Radiology, Nuclear Medicine and imaging,Signal Processing,Algebra and Number Theory,Analysis

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