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Manifold Fitting under Unbounded Noise
[article]
2023
arXiv
pre-print
There has been an emerging trend in non-Euclidean statistical analysis of aiming to recover a low dimensional structure, namely a manifold, underlying the high dimensional data. Recovering the manifold requires the noise to be of certain concentration. Existing methods address this problem by constructing an approximated manifold based on the tangent space estimation at each sample point. Although theoretical convergence for these methods is guaranteed, either the samples are noiseless or the
arXiv:1909.10228v2
fatcat:n77jdac5lffilazmh7xadyfdu4