Monday, May 14, 2012

Manifold learning on handwritten digits with Isomap

The Isomap algorithm is an approach to manifold learning. Isomap seeks a lower dimensional embedding of a set of high dimensional data points estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors.
The scikit-learn library provides a great implmentation of the Isomap algorithm and a dataset of handwritten digits. In this post we'll see how to load the dataset and how to compute an embedding of the dataset on a bidimentional space.
Let's load the dataset and show some samples:
from pylab import scatter,text,show,cm,figure
from pylab import subplot,imshow,NullLocator
from sklearn import manifold, datasets

# load the digits dataset
# 901 samples, about 180 samples per class 
# the digits represented 0,1,2,3,4
digits = datasets.load_digits(n_class=5)
X =
color =

# shows some digits
for i in range(36):
 ax = subplot(6,6,i)
 ax.xaxis.set_major_locator(NullLocator()) # remove ticks
 imshow(digits.images[i], cmap=cm.gray_r) 
The result should be as follows:

Now X is a matrix where each row is a vector that represent a digit. Each vector has 64 elements and it has been obtained using spatial resampling on the above images. We can apply the Isomap algorithm on this data and plot the result with the following lines:
# running Isomap
# 5 neighbours will be considered and reduction on a 2d space
Y = manifold.Isomap(5, 2).fit_transform(X)

# plotting the result
scatter(Y[:,0], Y[:,1], c='k', alpha=0.3, s=10)
for i in range(Y.shape[0]):
 text(Y[i, 0], Y[i, 1], str(color[i]),
      color=cm.Dark2(color[i] / 5.),
      fontdict={'weight': 'bold', 'size': 11})
The new embedding for the data will be as follows:

We computed a bidimensional version of each pattern in the dataset and it's easy to see that the separation between the five classes in the new manifold is pretty neat.


  1. This first code snippet doesn't load anything. Any idea why?

    1. Hi, if you add show() at the end of the snippet, you will be able to see the image with the handwritten digits.