# Random Projection and Its Applications

arXiv: https://arxiv.org/abs/1710.03163 · HTML: https://mlsanity.com/paper/1710.03163/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

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

- [Balancing Utility and Privacy: Dynamically Private SGD with Random Projection](https://mlsanity.com/paper/2509.09485/) (2025)
- [FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection](https://mlsanity.com/paper/2509.10041/) (2025)

## Abstract (arXiv)

Random Projection is a foundational research topic that connects a bunch of machine learning algorithms under a similar mathematical basis. It is used to reduce the dimensionality of the dataset by projecting the data points efficiently to a smaller dimensions while preserving the original relative distance between the data points. In this paper, we are intended to explain random projection method, by explaining its mathematical background and foundation, the applications that are currently adopting it, and an overview on its current research perspective.