# Manifold Learning via Manifold Deflation

arXiv: https://arxiv.org/abs/2007.03315 · HTML: https://mlsanity.com/paper/2007.03315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

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

- [Manifold learning: what, how, and why](https://mlsanity.com/paper/2311.03757/) (2023)

## Abstract (arXiv)

Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on simple two-dimensional manifolds, due to problems such as vulnerability to noise, repeated eigendirections, holes in convex bodies, and boundary bias. We derive an embedding method for Riemannian manifolds that iteratively uses single-coordinate estimates to eliminate dimensions from an underlying differential operator, thus "deflating" it. These differential operators have been shown to characterize any local, spectral dimensionality reduction method. The key to our method is a novel, incremental tangent space estimator that incorporates global structure as coordinates are added. We prove its consistency when the coordinates converge to true coordinates. Empirically, we show our algorithm recovers novel and interesting embeddings on real-world and synthetic datasets.