Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction
This provides an efficient and interpretable unsupervised dimensionality reduction technique for handling high-dimensional data in machine learning and visualization, though it is incremental as it builds on existing graph-based methods.
The authors tackled the problem of dimensionality reduction by introducing a novel method based on hierarchical nearest neighbor graphs, which is competitive with t-SNE and UMAP in performance and visualization quality while being an order of magnitude faster in run-time.
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 https://github.com/koulakis/h-nne