t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this article, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.

中文翻译：

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t-viSNE：t-SNE投影的交互式评估和解释。

经证明，用于多维数据可视化的t分布随机邻居嵌入（t-SNE）是一种流行的方法，已在广泛的领域中得到了成功的应用。尽管它们有用，但t-SNE预测可能难以解释，甚至会产生误导，这会损害结果的可信度。了解t-SNE本身的细节以及其输出中的特定模式背后的原因可能是一项艰巨的任务，特别是对于降维方面的非专家而言。在本文中，我们介绍了t-viSNE，这是一种用于视觉探索t-SNE投影的交互式工具，它使分析人员能够检查其准确性和含义的各个方面，例如超参数的影响，距离和邻域的保留，密度和特定社区的成本，以及尺寸和视觉模式之间的相关性。我们为t-SNE投影的可视化提出了一个统一，可访问且高度集成的不同视图集合。通过带有真实数据集的假设使用场景来证明t-viSNE的适用性和可用性。最后，我们介绍了一项用户研究的结果，其中对该工具的有效性进行了评估。通过揭示运行t-SNE后通常会丢失的信息，我们希望支持分析人员使用t-SNE并使其结果更易于理解。我们提供了一项用户研究的结果，其中对该工具的有效性进行了评估。通过揭示运行t-SNE后通常会丢失的信息，我们希望支持分析人员使用t-SNE并使其结果更易于理解。我们提供了一项用户研究的结果，其中对该工具的有效性进行了评估。通过揭示运行t-SNE后通常会丢失的信息，我们希望支持分析人员使用t-SNE并使其结果更易于理解。