Practical Data Analysis Cookbook

Finding the principal components in your data using randomized PCA

PCA (and Kernel PCA) both use low-rank matrix approximation to estimate the principal components. The low-rank matrix approximation minimizes a cost function represented as a fit between a given matrix and its approximation.

Such a method might be really costly for big datasets. By randomizing how the singular value decomposition of the input dataset happens, the speed up in the estimation is significant.

Getting ready

To execute this recipe, you will need NumPy, Scikit, and Matplotlib. No other prerequisites are required.

How to do it…

As before, we create a wrapper method to estimate our model (the reduce_randomizedPCA.py file):

def reduce_randomizedPCA(x): ''' Reduce the dimensions ...