Dimensionality reduction is crucial both for visualization and preprocessing
high dimensional data for machine learning. We introduce a novel method based
on a hierarchy built on 1-nearest neighbor graphs in the original space which
is used to preserve the grouping properties of the data distribution on
multiple levels. The core of the proposal is an optimization-free projection
that is competitive with the latest versions of t-SNE and UMAP in performance
and visualization quality while being an order of magnitude faster in run-time.
Furthermore, its interpretable mechanics, the ability to project new data, and
the natural separation of data clusters in visualizations make it a general
purpose unsupervised dimension reduction technique. In the paper, we argue
about the soundness of the proposed method and evaluate it on a diverse
collection of datasets with sizes varying from 1K to 11M samples and dimensions
from 28 to 16K. We perform comparisons with other state-of-the-art methods on
multiple metrics and target dimensions highlighting its efficiency and
performance. Code is available at this https URL

提出了一种基于层次结构的 1 - 最近邻图的新方法，可以在保留数据分布多个级别的分组属性的同时，实现具有可解释机制、可视化品质高、运行速度快且可用于多种场景的非监督降维技术，并在不同规模、不同维度的多个数据集上进行了性能比较。