Graph-based Neural Acceleration for Nonnegative Matrix Factorization

Jens Sjölund; Maria Bånkestad

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arXiv · 2202.00264

Graph-based Neural Acceleration for Nonnegative Matrix Factorization

Jens Sjölund·Maria Bånkestad

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Abstract

We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite graphs that is well-known in certain fields, e.g., sparse linear algebra, but has not yet been exploited to design graph neural networks for matrix computations. We first consider low-rank factorization more broadly and propose a graph representation of the problem suited for graph neural networks. Then, we focus on the task of nonnegative matrix factorization and propose a graph neural network that interleaves bipartite self-attention layers with updates based on the alternating direction method of multipliers. Our empirical evaluation on synthetic and two real-world datasets shows that we attain substantial acceleration, even though we only train in an unsupervised fashion on smaller synthetic instances.

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BibTeX RIS

Jens Sjölund, Maria Bånkestad. 2022-02-01. Graph-based Neural Acceleration for Nonnegative Matrix Factorization. https://arxiv.org/abs/2202.00264

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