论文标题
基于图的神经加速度非负基质分解
Graph-based Neural Acceleration for Nonnegative Matrix Factorization
论文作者
论文摘要
我们描述了一种基于图的神经加速技术，用于非负矩阵分解，该技术建立在矩阵和两部分图之间的连接基于在某些领域中众所周知的，例如稀疏线性代数，但尚未利用用于设计矩阵计算的图形神经网络。我们首先更广泛地考虑低级分解，并提出适用于图形神经网络的问题的图表。然后，我们专注于非负矩阵分解的任务，并提出了一个图形神经网络，该图神网络将双方自发层与基于乘数交替方向方法进行更新。我们对合成和两个现实世界数据集的经验评估表明，即使我们仅在较小的合成实例上以无监督的方式进行训练，我们也达到了实质性的加速。
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.
