# Binary Random Projections with Controllable Sparsity Patterns

arXiv: https://arxiv.org/abs/2006.16180 · HTML: https://mlsanity.com/paper/2006.16180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

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

- [Distributed clustering in partially overlapping feature spaces](https://mlsanity.com/paper/2510.09799/) (2025)

## Abstract (arXiv)

Random projection is often used to project higher-dimensional vectors onto a lower-dimensional space, while approximately preserving their pairwise distances. It has emerged as a powerful tool in various data processing tasks and has attracted considerable research interest. Partly motivated by the recent discoveries in neuroscience, in this paper we study the problem of random projection using binary matrices with controllable sparsity patterns. Specifically, we proposed two sparse binary projection models that work on general data vectors. Compared with the conventional random projection models with dense projection matrices, our proposed models enjoy significant computational advantages due to their sparsity structure, as well as improved accuracies in empirical evaluations.