# Contrastive independent component analysis

arXiv: https://arxiv.org/abs/2407.02357 · HTML: https://mlsanity.com/paper/2407.02357/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning](https://mlsanity.com/paper/1805.08651/) (2018)
- [Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA](https://mlsanity.com/paper/1605.06336/) (2016)
- [Unpaired Multi-Domain Causal Representation Learning](https://mlsanity.com/paper/2302.00993/) (2023)
- [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)
- [Subspace power method for symmetric tensor decomposition](https://mlsanity.com/paper/1912.04007/) (2019)
- [Moment Matching Deep Contrastive Latent Variable Models](https://mlsanity.com/paper/2202.10560/) (2022)
- [Probabilistic Contrastive Principal Component Analysis](https://mlsanity.com/paper/2012.07977/) (2020)
- [Contrastive Principal Component Analysis](https://mlsanity.com/paper/1709.06716/) (2017)
- [Landscape analysis of an improved power method for tensor decomposition](https://mlsanity.com/paper/2110.15821/) (2021)
- [On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis](https://mlsanity.com/paper/2206.06593/) (2022)
- [Toward the Identifiability of Comparative Deep Generative Models](https://mlsanity.com/paper/2401.15903/) (2024)
- [NPSA: Nonorthogonal Principal Skewness Analysis](https://mlsanity.com/paper/1907.09811/) (2019)

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

- [Multi-context principal component analysis](https://mlsanity.com/paper/2601.15239/) (2026)
- [Disentangling Shared and Target-Enriched Topics via Background-Contrastive Non-negative Matrix Factorization](https://mlsanity.com/paper/2602.22387/) (2026)

## Abstract (arXiv)

In recent years, there has been growing interest in jointly analyzing a foreground dataset, representing an experimental group, and a background dataset, representing a control group. The goal of such contrastive investigations is to identify salient features in the experimental group relative to the control. Independent component analysis (ICA) is a powerful tool for learning independent patterns in a dataset. We generalize it to contrastive ICA (cICA). For this purpose, we devise a new linear algebra based tensor decomposition algorithm, which is more expressive but just as efficient and identifiable as other linear algebra based algorithms. We establish the identifiability of cICA and demonstrate its performance in finding patterns and visualizing data, using synthetic, semi-synthetic, and real-world datasets, comparing the approach to existing methods.