Brief Visual Explanation of PCA

Posted by Ahmet Cecen on October 4, 2016

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Ahmet Cecen

Data Scientist / Materials Informatics

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Why do we use PCA?

  • Objective and hierarchical identification of most characteristic features.
  • Features are independent and uninformed.

PCA is an orthogonal coordinate system transformation that prioritizes maximum variance.

Here what that statement looks like in 2D.


The following is a 3D example showing the same effect in a different sense.