Numerical Computing with Python

SVD applied on handwritten digits using scikit-learn

SVD can be applied on the same handwritten digits data to perform an apple-to-apple comparison of techniques.

# SVD 
>>> import matplotlib.pyplot as plt 
>>> from sklearn.datasets import load_digits 
 
>>> digits = load_digits() 
>>> X = digits.data 
>>> y = digits.target 

In the following code, 15 singular vectors with 300 iterations are used, but we encourage the reader to change the values and check the performance of SVD. We have used two types of SVD functions, as a function randomized_svd provide the decomposition of the original matrix and a TruncatedSVD can provide total variance explained ratio. In practice, uses may not need to view all the decompositions and they can just use the