Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling
This addresses the need for improved hierarchical topic modeling in fields like document classification and bioinformatics, but it is incremental as it builds on existing NMF methods.
The paper tackled the problem of detecting latent hierarchical structure in data by introducing Neural NMF, a method based on nonnegative matrix factorization, and demonstrated that it outperforms other hierarchical NMF methods on synthetic and real datasets, offering better learned hierarchical structure and interpretability.
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.