Principal Component Analysis (PCA) is a tool that's used primarily for dimensionality reduction. We can use this to look at a dataset and find which dimensions and linear subspaces are the most salient. While there are several ways to implement this, we will show you how to perform PCA using SVD.
We'll do this as follows—we will work with a dataset that exists in 10 dimensions. We will start by creating two vectors that are heavily weighted in the front, and 0 otherwise:
vals = [ np.float32([10,0,0,0,0,0,0,0,0,0]) , np.float32([0,10,0,0,0,0,0,0,0,0]) ]
We will then add 9,000 additional vectors: 6,000 of these will be the same as the first two vectors, only with a little added random white noise, ...
