{"id":16017476,"url":"https://github.com/lucasrodes/kpca-denoising-python","last_synced_at":"2025-03-18T03:31:08.773Z","repository":{"id":71580839,"uuid":"76567044","full_name":"lucasrodes/kPCA-denoising-python","owner":"lucasrodes","description":"Reproduction of the experiments presented in Kernel PCA and De-noising in Feature Spaces, as a project in DD2434 Machine Learning Advance Course during Winter 2016","archived":false,"fork":false,"pushed_at":"2017-03-30T08:13:57.000Z","size":4171,"stargazers_count":15,"open_issues_count":1,"forks_count":11,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-02-28T06:31:55.587Z","etag":null,"topics":["denoising-images","kernel-methods","kpca-analysis","machine-learning","pca-analysis"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lucasrodes.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-12-15T14:32:44.000Z","updated_at":"2022-04-10T06:42:31.000Z","dependencies_parsed_at":"2023-05-15T05:45:11.668Z","dependency_job_id":null,"html_url":"https://github.com/lucasrodes/kPCA-denoising-python","commit_stats":{"total_commits":65,"total_committers":5,"mean_commits":13.0,"dds":0.3538461538461538,"last_synced_commit":"db36044c9a4264159d97f6ac8d5e298c8557aed7"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucasrodes%2FkPCA-denoising-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucasrodes%2FkPCA-denoising-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucasrodes%2FkPCA-denoising-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucasrodes%2FkPCA-denoising-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lucasrodes","download_url":"https://codeload.github.com/lucasrodes/kPCA-denoising-python/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243896580,"owners_count":20365398,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["denoising-images","kernel-methods","kpca-analysis","machine-learning","pca-analysis"],"created_at":"2024-10-08T16:06:08.365Z","updated_at":"2025-03-18T03:31:08.767Z","avatar_url":"https://github.com/lucasrodes.png","language":"Python","readme":"# Kernel PCA for denoising\nProject in [DD2434 Machine Learning Advance Course](http://www.kth.se/student/kurser/kurs/DD2434?l=en), Winter 2016.\n\n## Our team\n\n| Name                 | GitHub                                            |\n|----------------------|---------------------------------------------------|\n| Federico Baldassarre | [baldassarreFe](https://github.com/baldassarreFe) |\n| Zacharie Brodard     | [zach-b](https://github.com/zach-b)               |\n| Alfredo Fanghella    | [alfredojf](https://github.com/alfredojf)         |\n| Lucas Rodés          | [lucasrodes](https://github.com/lucasrodes)       |\n\n## Our work\nWe reproduced the experiments presented in the paper [Kernel PCA and De-noising in Feature Spaces](docs/paper.pdf) by Sebastian Mika, Bernhard Schölkopf, Alex Smola Klaus-Robert Müller, Matthias Scholz and Gunnar Rätsch. In this regard, you can read our [report](docs/report.pdf) and our [presentation](docs/presentation.pdf).\n\n\n## Dependencies\n\nIn order to run the experiments, make sure you have all dependencies installed\n\n* matplotlib (\u003e= 2.0.0)\n* pandas (\u003e=0.19.2)\n* rpy2 (\u003e=2.8.5)\n* scikit-image (\u003e=0.12.3)\n* scipy (\u003e=0.19.0)\n* numpy (\u003e=1.12.1)\n* sklearn (\u003e=0.0)\n\nYou can install them by typing\n```\npip3 install -r requirements.txt\n```\n\nWe strongly recommend using a [virtual environment](http://docs.python-guide.org/en/latest/dev/virtualenvs/) in order to keep these dependencies isolated from the rest of the system. Follow the instructions [here](files/setupvenv.md) to set up you virtual environment.\n\n## Running the experiments\n\nIn the paper, there are three major experiments:\n\n* Toy example: 11 Gaussians\n* Toy example: De-noising\n* Digit denoising (USPS Dataset)\n\nThe file [our_kpca.py](our_kpca.py) contains our own implementation of the kPCA method, based on the paper approach.\n\n### Toy example: 11 Gaussians\nThe code related to this example can be found in [example1.py](example1.py).\n\nRun the script as\n```\npython3 example1.py\n```\n\nBy default, this script outputs the kPCA MSE, PCA MSE and their ratio for 45 different settings of sigma.\n\n### Toy example: De-noising\nThe code related to this example can be found in [example2.py](example2.py)\n\nRun the script as\n```\npython3 example2.py\n```\n\nOnce the execution has ended, a picture as follows will be displayed.\n\n![alt text](files/figure_1.png)\n\nYou might get some warnings, just ignore them.\n\n### Digit denoising (USPS Dataset)\n\n\u003e :warning:\n  Known issue: the USPS dataset is no longer available at [mldata.org](mldata.org), we will look into an alternative source\n\nThe code related to this example can be found in [example3.py](example3.py)\n\nRun the script as\n```\npython3 example3.py\n```\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucasrodes%2Fkpca-denoising-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucasrodes%2Fkpca-denoising-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucasrodes%2Fkpca-denoising-python/lists"}