# Effective Estimation of the Dimensions of a Manifold from Random Samples

arXiv: https://arxiv.org/abs/2209.01839 · HTML: https://mlsanity.com/paper/2209.01839/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [CA-PCA: Manifold Dimension Estimation, Adapted for Curvature](https://mlsanity.com/paper/2309.13478/) (2023)

## Abstract (arXiv)

We give explicit theoretical and heuristical bounds for how big does a data set sampled from a reach-1 submanifold M of euclidian space need to be, to be able to estimate the dimension of M with 90% confidence.