# Ridge Estimation with Nonlinear Transformations

arXiv: https://arxiv.org/abs/2306.05722 · HTML: https://mlsanity.com/paper/2306.05722/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Spectral Methods for Data Science: A Statistical Perspective](https://mlsanity.com/paper/2012.08496/) (2020)
- [Efficient Manifold and Subspace Approximations with Spherelets](https://mlsanity.com/paper/1706.08263/) (2017)
- [Manifold Fitting under Unbounded Noise](https://mlsanity.com/paper/1909.10228/) (2019)
- [A Comprehensive Approach to Mode Clustering](https://mlsanity.com/paper/1406.1780/) (2014)
- [Quadratic Matrix Factorization with Applications to Manifold Learning](https://mlsanity.com/paper/2301.12965/) (2023)
- [Modal Regression using Kernel Density Estimation: a Review](https://mlsanity.com/paper/1710.07004/) (2017)
- [Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios](https://mlsanity.com/paper/1707.01711/) (2017)

## Abstract (arXiv)

Ridge estimation is an important manifold learning technique. The goal of this paper is to examine the effects of nonlinear transformations on the ridge sets. The main result proves the inclusion relationship between ridges: cR(fcirc p)subseteq cR(p), provided that the transformation f is strictly increasing and concave on the range of the function p. Additionally, given an underlying true manifold cM, we show that the Hausdorff distance between cR(fcirc p) and its projection onto cM is smaller than the Hausdorff distance between cR(p) and the corresponding projection. This motivates us to apply an increasing and concave transformation before the ridge estimation. In specific, we show that the power transformations fq(y)=yq/q,-∞<q≤ 1 are increasing and concave on RR_+, and thus we can use such power transformations when p is strictly positive. Numerical experiments demonstrate the advantages of the proposed methods.