# Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction

arXiv: https://arxiv.org/abs/2110.00317 · HTML: https://mlsanity.com/paper/2110.00317/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 127 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [Experiments with Random Projection](https://mlsanity.com/paper/1301.3849/) (2013)
- [Approximated and User Steerable tSNE for Progressive Visual Analytics](https://mlsanity.com/paper/1512.01655/) (2015)
- [Semi-Automatic Data Annotation guided by Feature Space Projection](https://mlsanity.com/paper/2007.13689/) (2020)
- [A Comprehensive Approach to Mode Clustering](https://mlsanity.com/paper/1406.1780/) (2014)
- [A survey of dimensionality reduction techniques based on random projection](https://mlsanity.com/paper/1706.04371/) (2017)
- [Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search](https://mlsanity.com/paper/1812.07484/) (2018)
- [Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs](https://mlsanity.com/paper/1909.12902/) (2019)

## Built on by (most cited first, 2 of 2)

- [Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections](https://mlsanity.com/paper/2201.06379/) (2022)
- [Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering](https://mlsanity.com/paper/2202.11667/) (2022)

## Abstract (arXiv)

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our `High-Dimensional Sharpened DR' (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog.