Performing Principal Components Analysis

Principal Components Analysis (PCA) is a dimensionality reduction technique that's used very frequently in computer vision and machine learning. When we deal with features with large dimensionalities, training a machine learning system becomes prohibitively expensive. Therefore, we need to reduce the dimensionality of the data before we can train a system. However, when we reduce the dimensionality, we don't want to lose the information present in the data. This is where PCA comes into the picture! PCA identifies the important components of the data and arranges them in the order of importance. You can learn more about it at http://dai.fmph.uniba.sk/courses/ml/sl/PCA.pdf. It is used a lot in face recognition systems. Let's see how to perform PCA on input data.

How to do it…

1. Create a new Python file, and import the following packages:

   import numpy as np
   from sklearn import decomposition 

2. Let's define five dimensions for our input data. The first two dimensions...