A small experiment in dimensionality reduction using an Autoencoder, with the results compared to that of Principal Component Analysis. The test dataset used is the Iris dataset (from Seaborn's built-in datasets).
The graph below shows a sample of the results obtained, illustrating he linear separability of the features in lower dimensions.
The Autoencoder is created using Keras' Sequential API, while the Principal Component decomposition is done using SciKit-Learn's built-in PCA functionality.
Install the necessary requirements by running:
pip install -r requirements.txt
Execute the autoencoder module by running:
python -m autoencoder
The script will produce a graph similar to those given in the results folder, and will save generated data
(principal components and encoded features) to a .csv file.
The __main__.py file inside the autoencoder module contains parameters which can be customised to change the number
of nodes in the hidden layers, the number of encoded features/principal components to generate (i.e. the reduced
dimension size of the patterns), etc.