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Documentation 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).

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Installation

PyPI install, our package is based on PyTorch, presuming you already have PyTorch installed

pip install ncut-pytorch

Install PyTorch if you haven't

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 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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