https://github.com/thefloatingstring/cpca-as-preprocessing-for-supervised-models
https://github.com/thefloatingstring/cpca-as-preprocessing-for-supervised-models
Last synced: 5 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/thefloatingstring/cpca-as-preprocessing-for-supervised-models
- Owner: TheFloatingString
- Created: 2023-10-10T21:52:55.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-12T04:49:31.000Z (over 2 years ago)
- Last Synced: 2025-03-02T21:16:42.145Z (11 months ago)
- Language: Jupyter Notebook
- Size: 7.41 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Improving Classification Accuracy using Contrastive Principal Component Analysis
Notebooks can be run using Google Colab. The Google Colab environment should automatically take care of all dependencies and data downloads.
### Notebooks
* `compare-cpca-with-other-methods.ipynb`: compares cPCA with other dimensionality reduction methods (runtime: ~34 seconds)
* `run-cpca-on-mnist-and-mouse-datasets.ipynb`: runs cPCA experiments on the MNIST and Mouse Down Syndrome Gene Expression datasets (runtime: ~244 seconds)
* `validate-cPCA-with-kNN.ipynb`: evalute k-NN 5-fold mean validation accuracy using PCA and cPCA pre-processed data (runtime: ~15 seconds)
### Runtime Instructions
For `run-cpca-on-mnist-and-mouse-datasets.ipynb`, the user would need to specify a JSON object containing a valid Kaggle username and password, as specified in the notebook instructions.
The other two notebooks can be run without any additional configuration.