# DimenFix: A novel meta-dimensionality reduction method for feature preservation

arXiv: https://arxiv.org/abs/2211.16752 · HTML: https://mlsanity.com/paper/2211.16752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [RankAxis: Towards a Systematic Combination of Projection and Ranking in Multi-Attribute Data Exploration](https://mlsanity.com/paper/2208.01493/) (2022)

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

- [EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models](https://mlsanity.com/paper/2406.17462/) (2024)

## Abstract (arXiv)

Dimensionality reduction has become an important research topic as demand for interpreting high-dimensional datasets has been increasing rapidly in recent years. There have been many dimensionality reduction methods with good performance in preserving the overall relationship among data points when mapping them to a lower-dimensional space. However, these existing methods fail to incorporate the difference in importance among features. To address this problem, we propose a novel meta-method, DimenFix, which can be operated upon any base dimensionality reduction method that involves a gradient-descent-like process. By allowing users to define the importance of different features, which is considered in dimensionality reduction, DimenFix creates new possibilities to visualize and understand a given dataset. Meanwhile, DimenFix does not increase the time cost or reduce the quality of dimensionality reduction with respect to the base dimensionality reduction used.