sude 0.1.5

A scalable manifold learning (SUDE) method that can cope with large-scale and high-dimensional data in an efficient manner.

Sampling-enabled scalable manifold learning unveils the discriminative cluster structure of high-dimensional data (SUDE)

We propose a scalable manifold learning (SUDE) method that can cope with large-scale and high-dimensional data in an efficient manner. It starts by seeking a set of landmarks to construct the low-dimensional skeleton of the entire data, and then incorporates the non-landmarks into this skeleton based on the constrained locally linear embedding.

This repository provides the Python version of SUDE. The MATLAB version can be found at https://github.com/ZPGuiGroupWhu/sude. The related paper has been published in Nature Machine Intelligence: https://www.nature.com/articles/s42256-025-01112-9.

Project layout

The project now follows the structure of the scikit-learn-contrib/project-template:

.
|-- .github/workflows/
|-- benchmarks/
|-- doc/
|-- examples/
|-- image/
|-- sude/
|   |-- __init__.py
|   |-- _sude.py
|   |-- _version.py
|   `-- tests/
|-- pyproject.toml
`-- README.md

Installation

Supported python versions are 3.8 and above.

This project has been uploaded to PyPI, supporting direct download and installation from pypi

pip install sude

Manual installation

git clone https://github.com/ZPGuiGroupWhu/SUDE-pkg.git
cd SUDE-pkg
pip install -e .

How to run

The package now exposes both a scikit-learn style estimator class and the legacy function wrapper.

Estimator interface

import numpy as np
from sude import SUDE
import time
import matplotlib.pyplot as plt

# Input data
data = np.loadtxt("benchmarks/rice.csv", delimiter=",")

# Obtain data size and true annotations
m = data.shape[1]
X = data[:, :m - 1]
ref = data[:, m - 1]

# Fit a scikit-learn style estimator
start_time = time.time()
model = SUDE(
    n_components=2,
    n_neighbors=10,
    init="pca",
    max_iter=50,
)
Y = model.fit_transform(X)
end_time = time.time()
print("Elapsed time:", end_time - start_time, 's')

plt.scatter(Y[:, 0], Y[:, 1], c=ref, cmap='tab10', s=4)
plt.show()

The estimator provides the familiar API:

model = SUDE(n_components=2, n_neighbors=10, init="spectral")
Y_train = model.fit_transform(X_train)
Y_test = model.transform(X_test)

Function interface

The original function entry point remains available for backwards compatibility:

from sude import sude

Y = sude(X, no_dims=2, k1=10, initialize="le", T_epoch=50)

Run the packaged example with:

uv run python examples/plot_sude_embedding.py

Run the test suite with:

uv run python -m unittest discover -s sude/tests

Citation request

Peng, D., Gui, Z., Wei, W. et al. Sampling-enabled scalable manifold learning unveils the discriminative cluster structure of high-dimensional data. Nat. Mach. Intell. (2025). https://doi.org/10.1038/s42256-025-01112-9

License

SUDE is released under the MIT License.