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Nonlocal infinity Laplacian equation on graphs with applications in image processing and machine learning

机译:图上的非局部无穷拉普拉斯方程及其在图像处理和机器学习中的应用

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摘要

In this paper, an adaptation of the infinity Laplacian equation to weighted graphs is proposed. This adaptation leads to a nonlocal partial difference equation on graphs, which is an extension of the well-known approximations of the infinity Laplacian equation. To do so, we study the limit as p tends to infinity of minimizers of p-harmonic function on graphs. We also prove the existence and uniqueness of the solution of this equation. Our motivation stems from the extension of the nonlocal infinity Laplacian equation from image processing to machine learning fields, with proposed illustrations for image inpainting and semi-supervised clustering.
机译:在本文中,提出了无穷拉普拉斯方程对加权图的适应。这种适应导致了图上的非局部偏差分方程,这是无穷拉普拉斯方程的众所周知近似的扩展。为此,我们研究极限,因为p趋于图上p谐波函数的极小值的无穷大。我们还证明了该方程解的存在性和唯一性。我们的动机来自将非局部无穷拉普拉斯方程从图像处理扩展到机器学习领域,并提出了用于图像修复和半监督聚类的插图。

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