发表机构
ENS Paris-Saclay（巴黎萨克雷高等师范学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍polyview，一个兼容scikit-learn的Python包，提供多视图嵌入、聚类、融合及不完整视图处理工具，在五个真实数据集上验证，旨在支持多视图方法的基准测试与原型设计。
AI 中文摘要
多视图学习联合利用同一数据的多个互补表示，在机器学习中变得越来越重要。然而，Python生态系统缺乏积极维护的、统一的工具来支持端到端的多视图工作流。在本文中，我们介绍了polyview，一个Python包，它提供了多视图嵌入、聚类、融合和视图增强的工具，以及处理不完整视图的功能，所有这些都与scikit-learn兼容。该库提供了一个统一接口，用于组合异构多视图工作流，包括在多视图和单视图阶段之间的无缝转换。它围绕一组核心类和实用程序构建，这些类和实用程序能够组合不同的方法，并直接实现新方法。我们在五个真实的多视图数据集上展示了该包，并将其基于典型相关分析的组件与两个已建立的库的组件进行了比较。polyview旨在成为多视图方法基准测试和原型设计的实用工具包，以及该领域未来研究和发展的基础。
英文摘要
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.
