Dimensionality reduction; a simple PCA example using python

Dimensionality reduction is a powerful approach to reduce inputs size, reduce training time and visualize data.

As an example, you can use PCA(Principal Component Analysis) or ICA (independent component analysis) or LLE (Locally Linear Embedding).

to see class grouping. You can try it on your data easily with python in a couple of lines.

import mdp

pca = mdp.pca(ds.data)

pylab.title("PCA")

pylab.plot(pca[:,0], pca[:,1], '.')

The figure presents the PCA dimensionally reduction applied on a digit dataset. You can find the source code here to see you to do a PCA, ICA or LLE using python. Unfortunately, ICA doesn't work on our dataset because it doesn't converge.