Neural nonnegative matrix factorization for hierarchical multilayer topic modeling

被引：0

作者：

Haddock, Jamie ^{[1
]}

Will, Tyler ^{[2
]}

Vendrow, Joshua ^{[3
]}

Zhang, Runyu ^{[4
]}

Molitor, Denali ^{[5
]}

Needell, Deanna ^{[6
]}

Gao, Mengdi ^{[7
]}

Sadovnik, Eli ^{[1
,2
,3
,4
,5
,6
,7
]}

机构：

[1] Harvey Mudd Coll, Dept Math, 301 Platt Blvd, Claremont, CA 91711 USA

[2] Optimal Dynam, New York, NY 10001 USA

[3] MIT, Dept EECS, 50 Vassar St, Cambridge, MA 02140 USA

[4] Harvard Univ, Sch Engn & Appl Sci, 150 Western Ave, Cambridge, MA 02138 USA

[5] Google, Seattle, WA 98103 USA

[6] Univ Calif Los Angeles, Dept Math, 520 Portola Plaza, Los Angeles, CA 90095 USA

[7] Schlumberger, Menlo Pk, CA 94025 USA

来源：

SAMPLING THEORY SIGNAL PROCESSING AND DATA ANALYSIS | 2024年 / 22卷 / 01期

关键词：

Hierarchical topic models; Nonnegative matrix factorization; Backpropagation; ALGORITHMS;

D O I：

10.1007/s43670-023-00077-3

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页数：38

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