Performing Kernel Principal Components Analysis

PCA is good at reducing the number of dimensions, but it works in a linear manner. If the data is not organized in a linear fashion, PCA fails to do the required job. This is where Kernel PCA comes into the picture. You can learn more about it at Let's see how to perform Kernel PCA on the input data and compare it to how PCA performs on the same data.

How to do it…

  1. Create a new Python file, and import the following packages:
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.decomposition import PCA, KernelPCA
    from sklearn.datasets import make_circles
  2. Define the seed value for the random number generator. This is needed ...

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