# Unsupervised learning with contrastive latent variable models

arXiv: https://arxiv.org/abs/1811.06094 · HTML: https://mlsanity.com/paper/1811.06094/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 15 of 15)

- [Moment Matching Deep Contrastive Latent Variable Models](https://mlsanity.com/paper/2202.10560/) (2022)
- [SepVAE: a contrastive VAE to separate pathological patterns from healthy ones](https://mlsanity.com/paper/2307.06206/) (2023)
- [Double InfoGAN for Contrastive Analysis](https://mlsanity.com/paper/2401.17776/) (2024)
- [Separating common from salient patterns with Contrastive Representation Learning](https://mlsanity.com/paper/2402.11928/) (2024)
- [Toward the Identifiability of Comparative Deep Generative Models](https://mlsanity.com/paper/2401.15903/) (2024)
- [Feature Selection in the Contrastive Analysis Setting](https://mlsanity.com/paper/2310.18531/) (2023)
- [Contrastive independent component analysis](https://mlsanity.com/paper/2407.02357/) (2024)
- [A Data-Driven Prism: Multi-View Source Separation with Diffusion Model Priors](https://mlsanity.com/paper/2510.05205/) (2025)
- [Capturing patterns of variation unique to a specific dataset](https://mlsanity.com/paper/2104.08157/) (2021)
- [Contrastive Dimension Reduction: A Systematic Review](https://mlsanity.com/paper/2510.11847/) (2025)
- [Joint estimation of high-dimensional spiked covariance matrices via a partially shared subspace](https://mlsanity.com/paper/2607.08123/) (2026)
- [Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder](https://mlsanity.com/paper/2307.00976/) (2023)
- [Disentangling Shared and Target-Enriched Topics via Background-Contrastive Non-negative Matrix Factorization](https://mlsanity.com/paper/2602.22387/) (2026)
- [CASL-VAE: Learning Structured Latent Variables from Unpaired Data for Semi-supervised Clustering and Paired Sample Generation](https://mlsanity.com/paper/2607.08254/) (2026)
- [Counterfactual Contrastive Analysis](https://mlsanity.com/paper/2608.19032/) (2026)

## Abstract (arXiv)

In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings.However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.