论文标题
GMGM：快速多轴高斯图形模型
GmGM: a Fast Multi-Axis Gaussian Graphical Model
论文作者
论文摘要
本文介绍了高斯多出模型，该模型是构建矩阵和张量变化数据的稀疏图表示的模型。我们通过在共享轴的几个张量中同时学习此表示，这是允许分析多模式数据集（例如在多摩尼克中遇到的轴），从而概括了在该领域的先前工作。我们的算法仅使用每个轴的单个特征分类，在非统一的情况下，在先前的工作中实现了数量级的加速顺序。这允许在大型多模式数据集（例如单细胞多摩变数据）上使用我们的方法，这在先前的方法方面具有挑战性。我们验证了合成数据和五个现实数据集的模型。
This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered in multi-omics. Our algorithm uses only a single eigendecomposition per axis, achieving an order of magnitude speedup over prior work in the ungeneralized case. This allows the use of our methodology on large multi-modal datasets such as single-cell multi-omics data, which was challenging with previous approaches. We validate our model on synthetic data and five real-world datasets.
