论文标题
游行：降低参数维度的框架
ParaDime: A Framework for Parametric Dimensionality Reduction
论文作者
论文摘要
Paradime是降低参数维度（DR）的框架。在参数DR中，对神经网络进行了训练，可以在低维空间中嵌入高维数据项，同时最小化目标函数。 Paradime以这样的想法为基础，即几种现代DR技术的目标功能是由转变的项目间关系引起的。它提供了一个通用界面，用于指定这些关系和转换，并定义它们如何在控制培训过程的损失中使用。通过此界面，Paradime统一了DR技术的参数版本，例如公制MDS，T-SNE和UMAP。它允许用户完全自定义DR流程的所有方面。我们展示了这种易于自定义的易用性，适用于尝试有趣的技术，例如混合分类/嵌入模型和监督的DR。这样，Paradime开辟了可视化高维数据的新可能性。
ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space while minimizing an objective function. ParaDime builds on the idea that the objective functions of several modern DR techniques result from transformed inter-item relationships. It provides a common interface for specifying these relations and transformations and for defining how they are used within the losses that govern the training process. Through this interface, ParaDime unifies parametric versions of DR techniques such as metric MDS, t-SNE, and UMAP. It allows users to fully customize all aspects of the DR process. We show how this ease of customization makes ParaDime suitable for experimenting with interesting techniques such as hybrid classification/embedding models and supervised DR. This way, ParaDime opens up new possibilities for visualizing high-dimensional data.
