Principal component analysis (PCA) is a technique to bring out strong patterns in a dataset by supressing variations. It is used to clean data sets to make it easy to explore and analyse.
In this article, we have explored the basic idea behind Principal Component Analysis by giving the basic background of where it came from and a 17 dimensional example.
We have demonstrated when to use Principal Component Analysis:
- reduce the number of variables
- ensure variables are independent of one another
- Okay with making independent variables less interpretable
For further details, explore the full article
This is a companion discussion topic for the original entry at http://iq.opengenus.org/basic-ideas-principal-component-analysis/