Principal Component Analysis (PCA), is a linear dimension reduction method, which represents high dimensional data with low dimensional code. The purpose of this blog post is to give readers a better knowledge of the math behind PCA, which helps to apply the method in a correct way and come up with better similar methods. It also links PCA to a well-known algorithm in machine learning, called AutoEncoder.
This is a Security Bloggers Network syndicated blog post authored by Cylance Blog. Read the original post at: Cylance Blog