# Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling

## TL;DR

Neural NMF is a novel layered approach based on nonnegative matrix factorization that uncovers hierarchical structures in data, improving interpretability and performance over existing methods across various datasets.

## Contribution

This paper introduces Neural NMF, a neural network-based recursive NMF method for hierarchical topic modeling, enhancing structure discovery and interpretability.

## Key findings

- Neural NMF outperforms existing hierarchical NMF methods.
- It provides better hierarchical structure learning.
- It improves interpretability of topics.

## 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.