EMBEDDING SIGNALS ON GRAPHS WITH UNBALANCED DIFFUSION EARTH MOVER'S DISTANCE.
EMBEDDING SIGNALS ON GRAPHS WITH UNBALANCED DIFFUSION EARTH MOVER'S DISTANCE.
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DOI：
10.1109/icassp43922.2022.9746556
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发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Krishnaswamy, Smita
中科院分区：
文献类型：
--
作者：
Tong, Alexander;Huguet, Guillaume;Shung, Dennis;Natik, Amine;Kuchroo, Manik;Lajoie, Guillaume;Wolf, Guy;Krishnaswamy, Smita
在现代关系机器学习中，经常会遇到通过许多领域中观察之间的交互或相似而产生的大图。此外，在许多情况下，用于分析的目标实体实际上是这种图上的信号。我们建议通过使用推土机距离(EMD)和基础图上的测地线成本来比较和组织这样的图形信号数据集。通常，EMD是通过优化在底层度量空间上将一个概率分布传输到另一个概率分布的成本来计算的。然而，当计算多个信号之间的EMD时，这是低效的。在这里，我们提出了一种非平衡图EMD，它有效地将底层图上的非平衡EMD嵌入到L1空间中，其度量我们称为不平衡扩散地球移动距离(UDEMD)。接下来，我们将展示这是如何给出抗噪能力较强的图形信号之间的距离的。最后，我们将其应用于根据临床记录组织患者，在基因图上嵌入建模为信号的细胞，并在大型细胞图上组织建模为信号的基因。在每种情况下，我们都表明，与其他方法相比，基于UDEMD的嵌入可以找到高效的精确距离。
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover’s distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an L1 space, whose metric we call unbalanced diffusion earth mover’s distance (UDEMD). Next, we show how this gives distances between graph signals that are robust to noise. Finally, we apply this to organizing patients based on clinical notes, embedding cells modeled as signals on a gene graph, and organizing genes modeled as signals over a large cell graph. In each case, we show that UDEMD-based embeddings find accurate distances that are highly efficient compared to other methods.
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DOI：
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发表时间：
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影响因子：
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