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The Google Colab environment should automatically take care of all dependencies and data downloads. \n\n### Notebooks\n\n* `compare-cpca-with-other-methods.ipynb`: compares cPCA with other dimensionality reduction methods (runtime: ~34 seconds)\n* `run-cpca-on-mnist-and-mouse-datasets.ipynb`: runs cPCA experiments on the MNIST and Mouse Down Syndrome Gene Expression datasets (runtime: ~244 seconds)\n* `validate-cPCA-with-kNN.ipynb`: evalute k-NN 5-fold mean validation accuracy using PCA and cPCA pre-processed data (runtime: ~15 seconds)\n\n### Runtime Instructions\n\nFor `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.\n\nThe other two notebooks can be run without any additional configuration.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthefloatingstring%2Fcpca-as-preprocessing-for-supervised-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthefloatingstring%2Fcpca-as-preprocessing-for-supervised-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthefloatingstring%2Fcpca-as-preprocessing-for-supervised-models/lists"}