Weighted nonlocal laplacian on interpolation from sparse data
Inspired by the nonlocal methods in image processing and the point integral method, we
introduce a novel weighted nonlocal Laplacian method to compute a continuous
interpolation function on a point cloud in high dimensional space. The numerical results in
semi-supervised learning and image inpainting show that the weighted nonlocal Laplacian
is a reliable and efficient interpolation method. In addition, it is fast and easy to implement.
introduce a novel weighted nonlocal Laplacian method to compute a continuous
interpolation function on a point cloud in high dimensional space. The numerical results in
semi-supervised learning and image inpainting show that the weighted nonlocal Laplacian
is a reliable and efficient interpolation method. In addition, it is fast and easy to implement.
Abstract
Inspired by the nonlocal methods in image processing and the point integral method, we introduce a novel weighted nonlocal Laplacian method to compute a continuous interpolation function on a point cloud in high dimensional space. The numerical results in semi-supervised learning and image inpainting show that the weighted nonlocal Laplacian is a reliable and efficient interpolation method. In addition, it is fast and easy to implement.
Springer
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