Dimensionality reduction (DR) techniques help analysts to understand patterns in high-dimensional spaces. These techniques, often represented by scatter plots, are employed in diverse science domains and facilitate similarity analysis among clusters and data samples. For datasets containing many granularities or when analysis follows the information visualization mantra, hierarchical DR techniques are the most suitable approach since they present major structures beforehand and details on demand. However, current hierarchical DR techniques are not fully capable of addressing literature problems because they do not preserve the projection mental map across hierarchical levels or are not suitable for most data types. This work presents HUMAP, a novel hierarchical dimensionality reduction technique designed to be flexible on preserving local and global structures and preserve the mental map throughout hierarchical exploration. We provide empirical evidence of our technique's superiority compared with current hierarchical approaches and show two case studies to demonstrate its strengths.
翻译:减少尺寸(DR)技术有助于分析者了解高维空间的格局,这些技术通常以散射地块为代表,用于不同的科学领域,便于对集群和数据样品进行相似性分析;对于包含许多颗粒的数据集,或分析遵循信息可视化符,分级DR技术是最合适的方法,因为它们事先提供主要结构和需求细节;然而,目前的分级DR技术无法完全解决文学问题,因为它们不保存跨等级的预测心理图,或不适合大多数数据类型;这项工作提出了HUMAPA,这是一种新的等级化减少维度技术,旨在灵活地保护当地和全球结构,在整个等级探索中保存精神图;我们提供了我们技术优于现有等级方法的经验证据,并展示了两个案例研究,以展示其优势。