# Contrastive Dimension Reduction: A Systematic Review

arXiv: https://arxiv.org/abs/2510.11847 · HTML: https://mlsanity.com/paper/2510.11847/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [Contrastive Variational Autoencoder Enhances Salient Features](https://mlsanity.com/paper/1902.04601/) (2019)
- [Unsupervised learning with contrastive latent variable models](https://mlsanity.com/paper/1811.06094/) (2018)
- [Probabilistic Contrastive Principal Component Analysis](https://mlsanity.com/paper/2012.07977/) (2020)
- [Contrastive latent variable modeling with application to case-control sequencing experiments](https://mlsanity.com/paper/2102.06731/) (2021)
- [Feature Selection in the Contrastive Analysis Setting](https://mlsanity.com/paper/2310.18531/) (2023)
- [Contrastive inverse regression for dimension reduction](https://mlsanity.com/paper/2305.12287/) (2023)
- [Contrastive linear regression](https://mlsanity.com/paper/2401.03106/) (2024)
- [Contrastive CUR: Interpretable Joint Feature and Sample Selection for Case-Control Studies](https://mlsanity.com/paper/2508.11557/) (2025)

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

- [Joint estimation of high-dimensional spiked covariance matrices via a partially shared subspace](https://mlsanity.com/paper/2607.08123/) (2026)

## Abstract (arXiv)

Contrastive dimension reduction (CDR) methods aim to extract signal unique to or enriched in a treatment (foreground) group relative to a control (background) group. This setting arises in many scientific domains, such as genomics, imaging, and time series analysis, where traditional dimension reduction techniques such as Principal Component Analysis (PCA) may fail to isolate the signal of interest. In this review, we provide a systematic overview of existing CDR methods. We propose a pipeline for analyzing case-control studies together with a taxonomy of CDR methods based on their assumptions, objectives, and mathematical formulations, unifying disparate approaches under a shared conceptual framework. We highlight key applications and challenges in existing CDR methods, and identify open questions and future directions. By providing a clear framework for CDR and its applications, we aim to facilitate broader adoption and motivate further developments in this emerging field.