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Unsupervised Learning
A comparison of the original and reduced data shape is done in the following code:
print('original data shape',x_train.shape)
print('reduced data shape', pca.shape)
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original data shape (60000, 784)
reduced data shape (60000, 3)
Finally let us plot the data points in the three-dimensional space.
Set = sns.color_palette("Set2", 10)
color_mapping = {key:value for (key,value) in enumerate(Set)}
colors = list(map(lambda x: color_mapping[x], y_train))
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(pca[:, 0], pca[:, 1],pca[:, 2], c=colors)
You can see that the points corresponding to the same color and hence same label
are clustered together. We have therefore successfully used PCA to reduce the
dimensions of MNIST images. Each original image was of size 28 × 28. Using
the PCA method we can reduce it to a smaller size. Normally for image data,
dimensionality reduction is necessary. This is because images are large in size
and contain a significant amount of redundant data.
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