Principal Component Analysis 4 Dummies: Eigenvectors, Eigenvalues and Dimension Reduction

NOTE: I am currently doing freelance consulting work for machine learning solutions. For all enquires please contact me at george(dot)m(dot)dallas@gmail(dot)com (replace (dot) with a . – ┬áthis is to prevent spam bots)

Having been in the social sciences for a couple of weeks it seems like a large amount of quantitative analysis relies on Principal Component Analysis (PCA). This is usually referred to in tandem with eigenvalues, eigenvectors and lots of numbers. So what’s going on? Is this just mathematical jargon to get the non-maths scholars to stop asking questions? Maybe, but it’s also a useful tool to use when you have to look at data. This post will give a very broad overview of PCA, describing eigenvectors and eigenvalues (which you need to know about to understand it) and showing how you can reduce the dimensions of data using PCA. As I said it’s a neat tool to use in information theory, and even though the maths is a bit complicated, you only need to get a broad idea of what’s going on to be able to use it effectively. Continue reading

Advertisements