# High-dimensional clustering via Random Projections

arXiv: https://arxiv.org/abs/1909.10832 · HTML: https://mlsanity.com/paper/1909.10832/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning](https://mlsanity.com/paper/2304.09154/) (2023)
- [Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II -- Clustering Extremely High-Dimensional Grid-Based Data](https://mlsanity.com/paper/2307.01400/) (2023)
- [Random-projection ensemble dimension reduction](https://mlsanity.com/paper/2410.04922/) (2024)
- [Sparse clustering via the Deterministic Information Bottleneck algorithm](https://mlsanity.com/paper/2601.20628/) (2026)

## Abstract (arXiv)

In this work, we address the unsupervised classification issue by exploiting the general idea of Random Projection Ensemble. Specifically, we propose to generate a set of low dimensional independent random projections and to perform model-based clustering on each of them. The top B^* projections, i.e. the projections which show the best grouping structure are then retained. The final partition is obtained by aggregating the clusters found in the projections via consensus. The performances of the method are assessed on both real and simulated datasets. The obtained results suggest that the proposal represents a promising tool for high-dimensional clustering.