Efficient Mean Curvature Computation on High-Dimensional Data Manifolds 事件

Efficient Mean Curvature Computation on High-Dimensional Data Manifolds arXiv:2606.06329v1 Announce Type: cross Abstract: Estimating local mean curvature at each point of a high-dimensional dataset is a key ingredient of geometry-aware machine learning algorithms, such as the Mean Curvature Boundary Points (MCBP) method. The naive implementation of this computation, based on a local shape operator approximated from k-nearest neighbor patches, involves an explicit construction of a matrix $H$ wh