https://github.com/lucasrodes/kpca-denoising-python
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
https://github.com/lucasrodes/kpca-denoising-python
denoising-images kernel-methods kpca-analysis machine-learning pca-analysis
Last synced: 10 months ago
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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
- Host: GitHub
- URL: https://github.com/lucasrodes/kpca-denoising-python
- Owner: lucasrodes
- Created: 2016-12-15T14:32:44.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-03-30T08:13:57.000Z (almost 9 years ago)
- Last Synced: 2025-02-28T06:31:55.587Z (11 months ago)
- Topics: denoising-images, kernel-methods, kpca-analysis, machine-learning, pca-analysis
- Language: Python
- Homepage:
- Size: 3.98 MB
- Stars: 15
- Watchers: 4
- Forks: 11
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Kernel PCA for denoising
Project in [DD2434 Machine Learning Advance Course](http://www.kth.se/student/kurser/kurs/DD2434?l=en), Winter 2016.
## Our team
| Name | GitHub |
|----------------------|---------------------------------------------------|
| Federico Baldassarre | [baldassarreFe](https://github.com/baldassarreFe) |
| Zacharie Brodard | [zach-b](https://github.com/zach-b) |
| Alfredo Fanghella | [alfredojf](https://github.com/alfredojf) |
| Lucas Rodés | [lucasrodes](https://github.com/lucasrodes) |
## Our work
We 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).
## Dependencies
In order to run the experiments, make sure you have all dependencies installed
* matplotlib (>= 2.0.0)
* pandas (>=0.19.2)
* rpy2 (>=2.8.5)
* scikit-image (>=0.12.3)
* scipy (>=0.19.0)
* numpy (>=1.12.1)
* sklearn (>=0.0)
You can install them by typing
```
pip3 install -r requirements.txt
```
We 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.
## Running the experiments
In the paper, there are three major experiments:
* Toy example: 11 Gaussians
* Toy example: De-noising
* Digit denoising (USPS Dataset)
The file [our_kpca.py](our_kpca.py) contains our own implementation of the kPCA method, based on the paper approach.
### Toy example: 11 Gaussians
The code related to this example can be found in [example1.py](example1.py).
Run the script as
```
python3 example1.py
```
By default, this script outputs the kPCA MSE, PCA MSE and their ratio for 45 different settings of sigma.
### Toy example: De-noising
The code related to this example can be found in [example2.py](example2.py)
Run the script as
```
python3 example2.py
```
Once the execution has ended, a picture as follows will be displayed.
You might get some warnings, just ignore them.
### Digit denoising (USPS Dataset)
> :warning:
Known issue: the USPS dataset is no longer available at [mldata.org](mldata.org), we will look into an alternative source
The code related to this example can be found in [example3.py](example3.py)
Run the script as
```
python3 example3.py
```