# RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections

arXiv: https://arxiv.org/abs/1405.1792 · HTML: https://mlsanity.com/paper/1405.1792/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2014.

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

- [Learning Augmentation Distributions using Transformed Risk Minimization](https://mlsanity.com/paper/2111.08190/) (2021)
- [Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning](https://mlsanity.com/paper/2102.12328/) (2021)
- [A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections](https://mlsanity.com/paper/2311.16614/) (2023)
- [On the Noise Sensitivity of the Randomized SVD](https://mlsanity.com/paper/2305.17435/) (2023)

## Abstract (arXiv)

In high dimensions, the classical Hotelling's T2 test tends to have low power or becomes undefined due to singularity of the sample covariance matrix. In this paper, this problem is overcome by projecting the data matrix onto lower dimensional subspaces through multiplication by random matrices. We propose RAPTT (RAndom Projection T-Test), an exact test for equality of means of two normal populations based on projected lower dimensional data. RAPTT does not require any constraints on the dimension of the data or the sample size. A simulation study indicates that in high dimensions the power of this test is often greater than that of competing tests. The advantage of RAPTT is illustrated on high-dimensional gene expression data involving the discrimination of tumor and normal colon tissues.