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

An intuitive explanation of PCA and Eigenvectors.
This post can be an example of what I call “The art of pedagogy and communication skills”, it is very difficult to write such an intuitive and high quality explanations.
Thanks George Dallas

George Dallas

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.

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About Rami Albatal

I am Data Scientist with advanced experience in Lifelogging, Machine Learning, Big Data Analytics and Computer Vision. I am currently working on Machine Learning technologies for Mobile Operators Subscribers Analytics to optimise targeting, recommendation and costumer care services. Co-founder and organiser of the Lifelogging@Dublin Meetup group, I am an active long-term Lifelogger with particular interest in human behaviour tracking and analytics.
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