# mvlearn: Multiview Machine Learning in Python

arXiv: https://arxiv.org/abs/2005.11890 · HTML: https://mlsanity.com/paper/2005.11890/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 7 of 7)

- [Parea: multi-view ensemble clustering for cancer subtype discovery](https://mlsanity.com/paper/2209.15399/) (2022)
- [Shared Independent Component Analysis for Multi-Subject Neuroimaging](https://mlsanity.com/paper/2110.13502/) (2021)
- [Shared representations in brains and models reveal a two-route cortical organization during scene perception](https://mlsanity.com/paper/2507.13941/) (2025)
- [Rethinking LoRA for Data Heterogeneous Federated Learning: Subspace and State Alignment](https://mlsanity.com/paper/2602.01746/) (2026)
- [Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)](https://mlsanity.com/paper/2301.01038/) (2023)
- [Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering](https://mlsanity.com/paper/2301.10772/) (2023)
- [Model alignment using inter-modal bridges](https://mlsanity.com/paper/2505.12322/) (2025)

## Abstract (arXiv)

As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years. However, no comprehensive package exists that enables non-specialists to use these methods easily. mvlearn is a Python library which implements the leading multiview machine learning methods. Its simple API closely follows that of scikit-learn for increased ease-of-use. The package can be installed from Python Package Index (PyPI) and the conda package manager and is released under the MIT open-source license. The documentation, detailed examples, and all releases are available at https://mvlearn.github.io/.