# Experiments with Random Projection

arXiv: https://arxiv.org/abs/1301.3849 · HTML: https://mlsanity.com/paper/1301.3849/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2013.

## Built on by (most cited first, 27 of 27)

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- [OPORP: One Permutation + One Random Projection](https://mlsanity.com/paper/2302.03505/) (2023)
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- [Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection](https://mlsanity.com/paper/2510.05782/) (2025)
- [Differential Privacy with Random Projections and Sign Random Projections](https://mlsanity.com/paper/2306.01751/) (2023)
- [nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces](https://mlsanity.com/paper/2302.11508/) (2023)
- [Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods](https://mlsanity.com/paper/2305.12283/) (2023)
- [Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction](https://mlsanity.com/paper/2110.00317/) (2021)
- [Randomized Projection Learning Method forDynamic Mode Decomposition](https://mlsanity.com/paper/2110.01718/) (2021)
- [A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS](https://mlsanity.com/paper/2111.09038/) (2021)
- [Position: Stop Making Unscientific AGI Performance Claims](https://mlsanity.com/paper/2402.03962/) (2024)
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- [Trajectory-Class-Aware Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.01440/) (2025)
- [High dimensional Bayesian Optimization via Condensing-Expansion Projection](https://mlsanity.com/paper/2408.04860/) (2024)
- [Distribution free optimality intervals for clustering](https://mlsanity.com/paper/2107.14442/) (2021)
- [Noisy ℓ0-Sparse Subspace Clustering on Dimensionality Reduced Data](https://mlsanity.com/paper/2206.11079/) (2022)
- [HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations](https://mlsanity.com/paper/2303.15604/) (2023)
- [Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing](https://mlsanity.com/paper/2306.04518/) (2023)
- [Random functions as data compressors for machine learning of molecular processes](https://mlsanity.com/paper/2509.17937/) (2025)
- [Exact Limits of Random Projections for Preserving Geometry: Distance Recovery, Nearest-Neighbor Rankings, and Covariance Shape in Gaussian Models](https://mlsanity.com/paper/2609.02155/) (2026)

## Abstract (arXiv)

Recent theoretical work has identified random projection as a promising dimensionality reduction technique for learning mixtures of Gausians. Here we summarize these results and illustrate them by a wide variety of experiments on synthetic and real data.