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A Riemannian approach to graph embedding

机译:黎曼图嵌入方法

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In this paper, we make use of the relationship between the Laplace-Beltrami operator and the graph Laplacian, for the purposes of embedding a graph onto a Riemannian manifold. To embark on this study, we review some of the basics of Riemannian geometry and explain the relationship between the Laplace-Beltrami operator and the graph Laplacian. Using the properties of Jacobi fields, we show how to compute an edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths on the manifold between nodes. For the particular case of a constant sectional curvature surface, we use the Kruskal coordinates to compute edge weights that are proportional to the geodesic distance between points. We use the resulting edge-weight matrix to embed the nodes of the graph onto a Riemannian manifold. To do this, we develop a method that can be used to perform double centring on the Laplacian matrix computed from the edge-weights. The embedding coordinates are given by the eigenvectors of the centred Laplacian. With the set of embedding coordinates at hand, a number of graph manipulation tasks can be performed. In this paper, we are primarily interested in graph-matching. We recast the graph-matching problem as that of aligning pairs of manifolds subject to a geometric transformation. We show that this transformation is Pro-crustean in nature. We illustrate the utility of the method on image matching using the COIL database. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:在本文中,为了将图嵌入到黎曼流形上,我们利用Laplace-Beltrami算子和图Laplacian之间的关系。为了进行这项研究,我们回顾了黎曼几何的一些基础知识,并解释了Laplace-Beltrami算子和图Laplacian之间的关系。利用Jacobi场的属性,我们展示了如何计算边权矩阵,其中元素反映与节点之间的流形上的测地路径相关的截面曲率。对于截面曲率恒定的特殊情况,我们使用Kruskal坐标来计算与点之间的测地线距离成比例的边权重。我们使用生成的边缘权重矩阵将图的节点嵌入到黎曼流形上。为此,我们开发了一种可用于对根据边缘权重计算出的拉普拉斯矩阵执行双重居中的方法。嵌入坐标由居中的拉普拉斯算子的特征向量给出。有了这组嵌入坐标,就可以执行许多图形操作任务。在本文中,我们主要对图匹配感兴趣。我们将图匹配问题重塑为对齐要经历几何变换的歧管对。我们证明了这种转变本质上是亲甲壳动物。我们说明了该方法在使用COIL数据库进行图像匹配方面的实用性。 (c)2006模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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