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Sketching Earth-Mover Distance on Graph Metrics

机译:在图形指标上草绘地球移动距离

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We develop linear sketches for estimating the Earth-Mover distance between two point sets, i.e., the cost of the minimum weight matching between the points according to some metric. While Euclidean distance and Edit distance are natural measures for vectors and strings respectively, Earth-Mover distance is a well-studied measure that is natural in the context of visual or metric data. Our work considers the case where the points are located at the nodes of an implicit graph and define the distance between two points as the length of the shortest path between these points. We first improve and simplify an existing result by Brody et al. [4] for the case where the graph is a cycle. We then generalize our results to arbitrary graph metrics. Our approach is to recast the problem of estimating Earth-Mover distance in terms of an ℓ_1 regression problem. The resulting linear sketches also yield space-efficient data stream algorithms in the usual way.
机译:我们开发了线性草图来估计两个点集之间的地球移动距离,即,根据某种度量,两个点之间的最小权重匹配的成本。欧几里得距离和编辑距离分别是向量和字符串的自然度量,而地球移动距离是一种经过充分研究的度量,在视觉或度量数据的上下文中是自然的。我们的工作考虑了点位于隐式图节点处的情况,并将两个点之间的距离定义为这些点之间的最短路径的长度。我们首先改善和简化了Brody等人的现有结果。 [4]对于图形是循环的情况。然后,我们将结果推广到任意图形指标。我们的方法是根据ℓ_1回归问题来重现估算地球移动距离的问题。生成的线性草图还可以按常规方式产生节省空间的数据流算法。

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