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.

翻译：ParaDime是一个用于参数化降维的参数框架。在参数化降维中，神经网络通过最小化目标函数将高维数据项嵌入低维空间。该框架基于以下理念：多种现代降维技术的目标函数均源自数据项间关系的变换。它提供了一个通用接口，用于指定这些关系与变换，以及定义它们如何融入控制训练过程的损失函数中。通过该接口，ParaDime统一了度量MDS、t-SNE和UMAP等降维技术的参数化版本，允许用户全面自定义降维过程的各个方面。我们展示了这种易定制性如何使ParaDime适用于混合分类/嵌入模型及监督降维等有趣技术的实验。由此，ParaDime为高维数据可视化开辟了新可能性。