https://github.com/lirongwu/gcml
Code for WACV 2022 paper "Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation"
https://github.com/lirongwu/gcml
clustering manifold-learning visualization
Last synced: 6 months ago
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Code for WACV 2022 paper "Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation"
- Host: GitHub
- URL: https://github.com/lirongwu/gcml
- Owner: LirongWu
- License: mit
- Created: 2021-10-07T08:38:51.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-12-16T04:23:01.000Z (almost 3 years ago)
- Last Synced: 2025-03-27T07:48:10.417Z (7 months ago)
- Topics: clustering, manifold-learning, visualization
- Language: Python
- Homepage:
- Size: 49.7 MB
- Stars: 19
- Watchers: 1
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Generalized Clustering and Multi-Manifold Learning (GCML)
The code includes the following modules:
* Datasets (MNIST-full, MNIST-test, USPS, Fashion-MNIST, Reuters-10k, HAR, and Pendigits)
* Training for GCML
* Evaluation metrics
* Visualisation
## Requirements
* pytorch == 1.3.1
* scipy == 1.3.1
* numpy == 1.18.5
* scikit-learn == 0.21.3
* matplotlib == 3.1.1
## Description
* main.py
* pretrain() -- Pretraining the model with self-reconstruction Loss
* train() -- End-to-end training of the GCML model
* test() -- Test generalization performance on out-of-sample (testing sample)
* autotrain.py -- Scripts for automatic testing on seven datasets
* dataset.py
* Dataset() -- Load data of selected dataset
* evaluation.py
* GetIndicator() -- Auxiliary tool for evaluating metric
* loss.py
* Loss_calculate() -- Calculate losses: ℒLIS, ℒrank, ℒAE, ℒalign
* model.py
* AutoEncoder() -- The architecture used in this work
* GCML() -- Calculation *Q* distribution and *P* distribution
* utils.py
* visualize() -- Auxiliary tools for visualizing intermediate results
* Clustering() -- For initializing the clustering centers
## Dataset
The datasets used in this paper are available in:
https://drive.google.com/file/d/1nNenJQVBJ-R4B6rs_K_YxGrVyZq4kAfz/view?usp=sharing
## Running the code
1. Install the required dependency packages
2. To get the results on seven datasets, run
```
python autotrain.py
```
3. To get the metrics and visualisation, refer to
```
../plots/dataset/pics/
```
where the *dataset* is one of the seven datasets (MNIST-full, MNIST-test, USPS, Fashion-MNIST, Reuters-10k, HAR, and Pendigits)
## Citation
If you find this project useful for your research, please use the following BibTeX entry.
```
@inproceedings{wu2022generalized,
title={Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation},
author={Wu, Lirong and Liu, Zicheng and Xia, Jun and Zang, Zelin and Li, Siyuan and Li, Stan Z},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={139--147},
year={2022}
}
```