Problem
Research questions and friction points this paper is trying to address.
multi-view learning
Python ecosystem
end-to-end workflows
unified tooling
polyview: A Python package for multi-view machine learning
🤖 AI Summary
This study addresses the absence of a unified multi-view learning toolchain within the Python ecosystem by proposing polyview, a scikit-learn-compatible library. Built upon a composable architecture centered around core classes, polyview provides unified interfaces for embedding, clustering, fusion, and missing-view handling. It facilitates the flexible composition of heterogeneous workflows and enables seamless transitions between multi-view and single-view processing stages. Experimental evaluations on five real-world datasets demonstrate that its components outperform existing baseline libraries. By filling the gap in end-to-end multi-view learning tools, this work offers an efficient and practical foundational framework for benchmarking and prototyping.
📝 Abstract
Multi-view learning jointly exploits multiple complementary representations of the same data and has become increasingly important in machine learning. However, the Python ecosystem lacks actively maintained, unified tooling for end-to-end multi-view workflows. In this paper, we present polyview, a Python package that provides tools for multi-view embedding, clustering, fusion, and view augmentation, as well as for handling incomplete views, all compatible with scikit-learn. The library offers a unified interface for composing heterogeneous multi-view workflows, including seamless transitions between multi-view and single-view stages. It is built around a core set of classes and utilities that enable composition of different methods and straightforward implementation of new ones. We illustrate the package on five real multi-view datasets and compare its components based on canonical correlation analysis with those of two established libraries. polyview aims to be both a practical toolkit for benchmarking and prototyping multi-view methods and a foundation for future research and development in this area.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-view learning
Python package
scikit-learn compatible
canonical correlation analysis
end-to-end workflow
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