Hands-On Image Processing with Python

2D projection and visualization

As we can see from the loaded dataset, it's a 64-dimensional dataset. Now let's use scikit-learn's PCA() function to find the two dominant principal components of this dataset and project the dataset along these two dimensions, followed by scatter-plotting the projected data with Matplotlib and with each data point representing an image (a digit), with the digits label represented by a unique color, using the following code block:

pca_digits=PCA(2)digits.data_proj = pca_digits.fit_transform(digits.data) print(np.sum(pca_digits.explained_variance_ratio_))# 0.28509364823696987plt.figure(figsize=(15,10))plt.scatter(digits.data_proj[:, 0], digits.data_proj[:, 1], lw=0.25, c=digits.target, edgecolor='k', s=100, ...