nystrom-ncut 0.4.2

Normalized Cut and Nyström Approximation

nystrom-ncut

PyTorch implementations of Normalized Cut (spectral clustering) for very large graphs, with two complementary approximations:

• Nyström extension — subsample anchor points, decompose the small anchor-anchor affinity block, and extrapolate to all remaining points (Fowlkes, Belongie, Chung, Malik 2004, Spectral Grouping Using the Nyström Method).
• Random Fourier features — replace the kernel with an explicit finite-dimensional feature map so the affinity is never materialized (Rahimi & Recht 2007, Random Features for Large-Scale Kernel Machines).

Both are built on the original normalized-cut formulation (Shi & Malik 2000, Normalized Cuts and Image Segmentation).

Installation

pip install nystrom-ncut

Requires Python >=3.10 and a working torch + pytorch3d installation. For a CUDA build of PyTorch, install it explicitly before installing this package, e.g.:

pip install torch --index-url https://download.pytorch.org/whl/cu121
pip install nystrom-ncut

For development:

pip install -e ".[dev]"

Quick start

Nyström Normalized Cut

import torch
from nystrom_ncut import NystromNCut, SampleConfig

# (B, H, W, C) features from some backbone
model_features = torch.rand(20, 64, 64, 768)
inp = model_features.reshape(-1, 768)

ncut = NystromNCut(
    n_components=100,
    affinity_type="rbf",
    sample_config=SampleConfig(method="fps", num_sample=10000),
)
eigvectors = ncut.fit_transform(inp)              # (20*64*64, 100)
eigvalues = ncut.eigenvalues_                     # (100,)
eigvectors = eigvectors.reshape(20, 64, 64, 100)

Random-feature Kernel Normalized Cut

import torch
from nystrom_ncut import KernelNCut

inp = torch.rand(20 * 64 * 64, 768)
kn = KernelNCut(
    n_components=100,
    kernel_dim=1024,
    affinity_type="rbf",
)
eigvectors = kn.fit_transform(inp)

Distance Realization (MDS-style embedding)

import torch
from nystrom_ncut import DistanceRealization

inp = torch.rand(10000, 768)
dr = DistanceRealization(n_components=64, distance_type="euclidean")
embedding = dr.fit_transform(inp)                 # (10000, 64)

Discretizing eigenvectors into clusters

AxisAlign implements the Yu & Shi 2003 multiclass discretization on top of the spectral embedding:

from nystrom_ncut import AxisAlign

aligner = AxisAlign(sort_method="norm")
hard_labels = aligner.fit_transform(eigvectors, hard=True)  # (N,) int cluster ids

Sampling

SampleConfig controls how anchor points are picked for the Nyström and kernel methods. Available methods are:

• "full" — use every point (no subsampling).
• "random" — uniform random subset.
• "fps" — farthest-point sampling on a low-rank PCA of the features (default for accuracy).
• "fps_recursive" — iteratively refine FPS anchors using the current spectral embedding.

from nystrom_ncut import SampleConfig

cfg = SampleConfig(method="fps", num_sample=10000, fps_dim=12)

Repository layout

src/nystrom_ncut/
├── kernel/             # Random-feature NCut
├── nystrom/            # Nystrom NCut + distance realization
├── transformer/        # OnlineTransformerSubsampleFit, AxisAlign, mixins
├── common.py
├── distance_utils.py
├── extrapolation.py    # KNN extrapolation of anchors to new points
├── coloring.py         # RGB visualization helpers
└── sampling_utils.py

Citation

If you find this useful, please cite the upstream NCUT writeup:

AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space. Huzheng Yang, James Gee, Jianbo Shi, 2024.

License

MIT.