==============================================
h-NNE: Hierarchical Nearest Neighbor Embedding
==============================================
A fast hierarchical dimensionality reduction algorithm.
h-NNE is a general purpose dimensionality reduction algorithm such as t-SNE and UMAP. It stands out for its speed,
simplicity and the fact that it provides a hierarchy of clusterings as part of its projection process. The algorithm is
inspired by the FINCH_ clustering algorithm. For more information on the structure of the algorithm, please look at `our
corresponding paper in ArXiv`__:
M. Saquib Sarfraz\*, Marios Koulakis\*, Constantin Seibold, Rainer Stiefelhagen.
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction. CVPR 2022.
.. __: https://arxiv.org/abs/2203.12997
.. _FINCH: https://github.com/ssarfraz/FINCH-Clustering
More details are available in the `project documentation`__.
.. __: https://hnne.readthedocs.io/en/latest/index.html
------------
Installation
------------
The project is available in PyPI. To install run:
``pip install hnne``
----------------
How to use h-NNE
----------------
The HNNE class implements the common methods of the sklearn interface.
+++++++++++++++++++++++++
Simple projection example
+++++++++++++++++++++++++
.. code:: python
import numpy as np
from hnne import HNNE
data = np.random.random(size=(1000, 256))
hnne = HNNE(dim=2)
projection = hnne.fit_transform(data)
++++++++++++++++++++++++++++
Projecting on new points
++++++++++++++++++++++++++++
.. code:: python
hnne = HNNE()
projection = hnne.fit_transform(data)
new_data_projection = hnne.transform(new_data)
-----
Demos
-----
The following demo notebooks are available:
1. `Basic Usage`_
2. `Multiple Projections`_
3. `Clustering for Free`_
4. `Monitor Quality of Network Embeddings`_
.. _Basic Usage: notebooks/demo1_basic_usage.ipynb
.. _Multiple Projections: notebooks/demo2_multiple_projections.ipynb
.. _Clustering for Free: notebooks/demo3_clustering_for_free.ipynb
.. _Monitor Quality of Network Embeddings: notebooks/demo4_monitor_network_embeddings.ipynb
--------
Citation
--------
If you make use of this project in your work, it would be appreciated if you cite the hnne paper:
.. code:: bibtex
@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}
}
If you make use of the clustering properties of the algorithm please also cite:
.. code:: bibtex
@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}
}
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