A Random Matrix Approach for Random Feature Maps
This page contains a simple demo using Python 3 of the theoretical results in the following paper:
On the Spectrum of Random Features Maps of High Dimensional Data
where recent advances in matrix matrix theory are used to understand the mechanism of random feature maps, in particular the choice of nonlinearity.
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
We strongly recommend you to use Jupyter nootbook to have a direct illustration within your web browsers: here.
- Zhenyu LIAO
- Ph.D. student at CentraleSupelec, Paris, France
- Website: https://zhenyu-liao.github.io/
- E-mail: zhenyu.liao@l2s.centralesupelec.fr
- Prof. Romain COUILLET
- Professor at CentraleSupelec, Paris, France
- Website: http://romaincouillet.hebfree.org/
- E-mail: romain.couillet@centralesupelec.fr