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---
title: ncut-pytorch
emoji: ✂️
colorFrom: yellow
colorTo: pink
sdk: gradio
sdk_version: 4.42.0
app_file: app.py
pinned: false
license: apache-2.0
---
Documentation [https://ncut-pytorch.readthedocs.io/](https://ncut-pytorch.readthedocs.io/)
## NCUT: Nyström Normalized Cut
**Normalized Cut**, aka. spectral clustering, is a graphical method to analyze data grouping in the affinity eigenvector space. It has been widely used for unsupervised segmentation in the 2000s.
**Nyström Normalized Cut**, is a new approximation algorithm developed for large-scale graph cuts, a large-graph of million nodes can be processed in under 10s (cpu) or 2s (gpu).
## Gallery
TODO
## Installation
PyPI install, our package is based on [PyTorch](https://pytorch.org/get-started/locally/), presuming you already have PyTorch installed
```shell
pip install ncut-pytorch
```
[Install PyTorch](https://pytorch.org/get-started/locally/) if you haven't
```shell
pip install torch
```
## Why NCUT
Normalized cut offers two advantages:
1. soft-cluster assignments as eigenvectors
2. hierarchical clustering by varying the number of eigenvectors
Please see [NCUT and t-SNE/UMAP](compare.md) for a full comparison.
> paper in prep, Yang 2024
>
> AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space, Huzheng Yang, James Gee\*, Jianbo Shi\*, 2024
>
> Normalized Cuts and Image Segmentation, Jianbo Shi and Jitendra Malik, 2000
>
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