Abstract

Principal Components Analysis (PCA) is a simple but powerful algorithm often used for data analysis, and is most often used for dimensionality reduction. Typically, PCA is used as a pre-processing step for many larger methods of analysis. It has numerous applications besides dimensionality reduction, including data compression and visualization. It is also a core method for many more powerful extensions. Besides applications, we cover theory, implementation, and use of the PCA method.

It's a multi-part series in which I am planning to cover the following:

1. What is PCA?
2. Theory and Knowledge
3. How it works?
4. Step By Step Explanation
5. Applications of PCA
6. Limitation and Extension of PCA