标题： 稀疏随机化近似法的正常周期 显示英文标题

标题： Sparse Randomized Approximation of Normal Cycles

摘要： 我们开发了一种用于形状的Normal-Cycles表示的压缩算法，该算法使用再生核希尔伯特空间中的Nystrom近似和Ridge Leverage Score采样。 我们的方法在压缩误差的收敛速率上具有理论保证，获得的近似值被证明对于下游任务如大型形变度量映射(LDDMM)框架中的非线性形状配准是有用的，即使在非常高的压缩比率下也是如此。 我们的算法在现代几何处理数据集上的大规模形状数据上进行了演示，并显示出快速且可扩展，且误差迅速衰减。 显示英文摘要

摘要： We develop a compression algorithm for the Normal-Cycles representations of shape, using the Nystrom approximation in Reproducing Kernel Hilbert Spaces and Ridge Leverage Score sampling. Our method has theoretical guarantees on the rate of convergence of the compression error, and the obtained approximations are shown to be useful for down-line tasks such as nonlinear shape registration in the Large Deformation Metric Mapping (LDDMM) framework, even for very high compression ratios. The performance of our algorithm is demonstrated on large-scale shape data from modern geometry processing datasets, and is shown to be fast and scalable with rapid error decay.