A Random Matrix Approach to Extreme Learning Machine
This page contains a simple demo using Python 3 of the theoretical results in the following paper:
where recent advances in matrix matrix theory are used to analyze the performance of randomly-connected single-layer neural nets (also referred in literatures as extreme learning machines).
About the code
Comparison between theory and practice is available for data from
- MNIST database
- Gaussian mixture model
for a dozen of commonly-used activation functions.
To be able to test this code requires the following:
- Python: tested with version 3.6
- Numpy and Scipy
- Matplotlib for visulazation
- Scikit-learn for MNIST dataset