mvlearn: Multiview Machine Learning in Python
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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/.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 May 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2022Parea: multi-view ensemble clustering for cancer subtype discoverycited by 7
- 2021Shared Independent Component Analysis for Multi-Subject Neuroimagingcited by 6
- 2025Shared representations in brains and models reveal a two-route cortical organization during scene perceptioncited by 2
- 2026Rethinking LoRA for Data Heterogeneous Federated Learning: Subspace and State Alignmentcited by 2
- 2023Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)cited by 1
- 2023Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clusteringcited by 1
- 2025Model alignment using inter-modal bridgescited by 0
- 2026Rethinking LoRA for Data Heterogeneous Federated Learning: Subspace and State Alignment2 Feb 2026
- 2025Shared representations in brains and models reveal a two-route cortical organization during scene perception18 Jul 2025
- 2025Model alignment using inter-modal bridges18 May 2025
- 2023Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering25 Jan 2023
- 2023Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)3 Jan 2023
- 2022Parea: multi-view ensemble clustering for cancer subtype discovery30 Sep 2022
- 2021Shared Independent Component Analysis for Multi-Subject Neuroimaging26 Oct 2021