What changed & why

• Space by cluster mass. Coarse-level anchors are laid out via a lightweight circle-packing step guided by PCA anchors and \(1\)-NN relations to produce acompact, overlap-free arrangement. Larger clusters get more area; small ones remain visible.

• Better global framing. A vectorized overlap resolver plus relaxed/capped \(k\)-NN edge targets yields tight layouts without blow-ups, reducing unused whitespace and artifacts.

• Drop-in for h-NNE. v2 runs on the top few FINCH levels, then hands the result to the standard h-NNE refinement. Deeper (fine) levels still use the original fast point-to-anchor updates.

• Note: When running v2 on large datasets (>= 1M points), it starts the tree layout by default after some level of FINCH with a minimum number of clusters (10-10,000) to enhance point spread.

Using it

• v2 is on by default. To use the legacy pipeline:

  HNNE(..., hnne_version="v1")

• Defaults generally work well. Optional advanced knobs (e.g., start_cluster_view) let you steer the look: choose a deeper level for a more uniform, zoomed-in spread, or a coarser (top) level for a global, zoomed-out view before refinement.

More technical details (algorithms, complexity, limitations) are in our arXiv paper (linked soon).

Key properties

• Fast & scalable. No global nonconvex optimization; point updates are vectorized and memory-friendly.

• Structure-aware zoom. Choose a parent level for global context, then expand children for detail.

• Labels included. FINCH provides cluster labels out of the box—useful for unlabeled data and structured visualization.

• Parameter-light. FINCH is parameter-free; h-NNE uses robust defaults.

See our corresponding CVPR 2022 paper for the original algorithm and the arXiv addendum for the v2 packing layer.

M. Saquib Sarfraz*, Marios Koulakis*, Constantin Seibold, Rainer Stiefelhagen. Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction. CVPR 2022.

More details are available in the project documentation.

import numpy as np
from hnne import HNNE

data = np.random.random(size=(1000, 256))

hnne = HNNE(n_components=2)
projection = hnne.fit_transform(data)

hnne = HNNE()
projection = hnne.fit_transform(data)

new_data_projection = hnne.transform(new_data)

@article{hnne,
  title={Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction},
  author={M. Saquib Sarfraz, Marios Koulakis, Constantin Seibold, Rainer Stiefelhagen},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022}
}

 @inproceedings{finch,
   author    = {M. Saquib Sarfraz and Vivek Sharma and Rainer Stiefelhagen},
   title     = {Efficient Parameter-free Clustering Using First Neighbor Relations},
   booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   pages = {8934--8943},
   year  = {2019}
}