Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

February 14, 2015

Principal Component Analysis – Explained Visually [Examples up to 17D]

Filed under: Principal Component Analysis (PCA),Visualization — Patrick Durusau @ 11:37 am

Principal Component Analysis – Explained Visually by Victor Powell.

From the website:

Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It’s often used to make data easy to explore and visualize.

Another stunning visualization (2D, 3D and 17D, yes, not a typo, 17D) from Explained Visually.

Probably not the top item in your mind on Valentine’s Day but you should bookmark it and return when you have more time. 😉

I first saw this in a tweet by Mike Loukides.

1 Comment

  1. […] Principal Component Analysis – Explained Visually by Victor Powell.From the website:Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset.  […]

    Pingback by Principal Component Analysis – Explained ... — February 17, 2015 @ 9:06 am

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